Revisiting evolutionary algorithms in feature selection and nonfuzzy/fuzzy rule based classification

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Volumn 3, Issue 2, 2013, Pages 83-108

  Dehuri, Satchidananda ^a   Ghosh, Ashish ^b  

^a Ajou University   (South Korea)

^b INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION RULE DISCOVERY; DOMAIN OF KNOWLEDGE; FEATURE SELECTION ALGORITHM; KNOWLEDGE DISCOVERY IN DATABASE; MULTI-OBJECTIVE GENETIC ALGORITHM; MULTI-OBJECTIVE PROBLEM; OPTIMIZATION PROBLEMS; RULE-BASED CLASSIFICATION;

GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION;

DATABASE SYSTEMS;

EID: 84879531800     PISSN: 19424787     EISSN: 19424795     Source Type: Journal    
DOI: 10.1002/widm.1087     Document Type: Article

References (217)

1. Piatetsky-Shapiro G. Knowledge discovery in real databases: a report on the IJCAR 89 workshop. AI Mag. 1991, 11(5):68-70.
2. Fortnow L. The status of the P versus NP problem. Commun 2009, 52(9):78-86.
3. Han J, Kamber M. Data Mining: Concepts and Techniques, 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006.
4. Kantardzic M. Data Mining: Concepts, Models, Methods, and Algorithms. New York: John Wiley & Sons, 2003.
5. Miller H, Han J., eds. Geographical Data Mining and Knowledge Discovery, 2nd ed. New York: John Wiley & Sons, 2009.
6. Kargupta H, Han J, Yu P, Motwani R, Kumar V., eds. Next Generation of Data Mining. Boca Raton, FL: CRC Press and Taylor & Francis, 2008.
7. Geng L, Hamilton HJ. Interestingness measures for data mining: a survey. ACM Comput Surv 2006, 38(3):1-32.
8. Goebel M, Gruenwald L. A review of software packages for data mining. SIGKDD Explor 1999, 1(1):20-32.
9. Haughton D, Deichmann J, Eshghi A, Sayek S, Teebagy N, Topi H. A survey of data mining and knowledge discovery software tools. Am Stat 2003, 57(4):290-309.
10. Mikut R, Reischl M. Data mining tools. WIREs Data Mining Knowl Discov 2011, 1:431-443.
11. Peters G, Weber R. Dynamic clustering with soft computing. WIREs Data Mining Knowl Discov 2012, 2:226-236.
12. Coello CA, Dehuri S, Ghosh S., eds. Swarm Intelligence for Multi-objective Problems in Data Mining. Heidelberg, Germany: Springer-Verlag, 2009.
13. Ghosh A, Dehuri S, Ghosh S., eds. Multi-objective Evolutionary Algorithms for Knowledge Di scovery in Databases. Heidelberg, Germany: Springer-Verlag, 2008.
14. Freitas AA. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Heidelberg, Germany: Springer-Verlag, 2002.
15. Alcal-Fdez J, Snchez L, Garca S, del Jesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, et al. KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 2009, 13(3):307-318.
16. Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning. Reading MA: Addison-Wesley, 1989.
17. Smith SF. A learning system based on genetic tive algorithm. Ph.D. dissertation, University of Pittsburgh, Pittsburgh, PA, 1980.
18. Smith SF. Flexible learning of problem solving heuristics through adaptive search. In: Proceedings of 8th International Joint Conference on Artificial Intelligence, 1983, 422-425.
19. Holland JH. Escaping brittleness: the possibilities of general purpose learning algorithms applied to parallel-rule based systems. Mach Learn: An Artif Intell Approach 1986, 2:593-623.
20. Booker LB. Goldberg DE, Holland JH. Classifier systems and genetic algorithms. Artif Intell 1989, 40(1-3):235-283.
21. Venturini G. SIA: a supervised inductive algorithm with genetic search for learning attributes based concepts. In: Proceedings of European Conference on Machine Learning. LNAI 667, Berlin, Germany: Springer-Verlag, 1993, 280-296.
22. Greene DP. Smith SF. Competition based induction of decision models from examples. Mach Learn 1993, 13(2-3), 229-257.
23. Huhn J, Hullermeier E. Fr3: a fuzzy rule learner for inducing reliable classifier. IEEE Trans Fuzzy Syst 2009, 17:138-149.
24. Hullermeier E. Fuzzy sets in machine learning and data mining. Appl Soft Comput J 2011, 11:1493-1505.
25. Hullermeier E. Fuzzy machine learning and data mining. WIREs Data Mining Knowl Discov 2011, 1:269-283.
26. Nisbet R, John E, Gary M. Handbook of Statistical Analysis & Data Mining Applications. New York: Academic Press/Elsevier, 2009.
27. Zaki MJ, Parthasarathy S, Ogihara M, Li W. New algorithms for fast discovery of association rules. Technical Report URCS-TR-651, July 1997.
28. Wang JTL. Zaki MJ, Toivonen HTT, Shasha D. Data Mining in Bioinformatics. Berlin, Germany: Springer-Verlag, 2004.
29. Alberto P-M. Gene expression modular analysis an overview from the data mining perspective. WIREs Data Mining Knowl Discov 2011, 1:381-396.
30. Wang J., ed. Data Mining in Health Care Applications. Hershey, PA: IGI Publishing, 2003.
31. Kokol P, Pohorec S, Stiglic G, Podgorelec V. Evolutionary design of decision trees for medical application. WIREs Data Mining Knowl Discov 2012, 2:237-254.
32. Mucherino A, Papajoirgji P, Pardalos P. Data Mining in Agriculture. Berlin, Germany: Springer-Verlag, 2009.
33. Wang XZ. Data mining and knowledge discovery for process monitoring and control. London: Springer-Verlag, 1999.
34. Sequeira K, Zaki MJ. ADMIT: anomaly-base data mining for intrusions. In: 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining;July 2002.
35. Lam K-Y, Hui L, Chung S-L . A data reduction method for intrusion detection. J Syst Software 1996, 33:101-108.
36. Lee W, Stolfo SJ. A framework for constructing features and models for intrusion detection systems. ACM Trans Inform Syst Security 2000, 3(4):227-261.
37. Oliveira M, Gama J, An overview of social network analysis. WIREs Data Mining Knowl Discov 2012, 2:99-115.
38. Nicolas G-P . Evolutionary selection for training set selection. WIREs Data Mining Knowl Discov 2011, 1:512-523.
39. Dy JG, Broadley CE. Feature selection for unsupervised learning, J Mach Learn Res 2004, 5(5):845-889.
40. Brill FZ, Brown DE, Martin WN. Fast genetic selection of features for neural network classifiers. IEEE Trans Neural Netw 1998, 3(2):324-328.
41. Mitra P, Murthy CA, Pal SK. Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 2002, 24(3):301-312.
42. Yang J, Honavar V. Feature subset selection using a genetic algorithm, IEEE Intell SystTheir Appl 1998, 13(2):44-49.
43. Siedlecki W, Sklansky J. A note on genetic algorithms for large-scale feature selection. Pattern Recogn Lett 1989, 10:335-347.
44. Ghosh A, Nath B. Multi-objective rule mining using genetic algorithms. Information Sciences 2004, 163:123-133.
45. Ghosh A, Dehuri S, Ghosh S. Eds. Multiobjective Association Rule Mining. Berlin, Germany: Springer-Verlag, 2008.
46. Tseng LY, Yang SB. A genetic approach to the automatic clustering problem. Pattern Recogn 2001, 34:415-424.
47. Krsihma K, Murty MN. Genetic k-means algorithms. IEEE Trans Syst Man Cybernet Cybern 1999, 29:433-439.
48. Lozano JA, Larranaga P. Applying genetic algorithms to search for the best hierarchical clustering of a dataset. Pattern Recogn Lett 1999, 20:911-918.
49. Hruschka ER, Campello RJGB, Freitas AA, deCarvalho A. A survey of evolutionary algorithms for clustering. IEEE Trans Syst Man Cybern C. 2009, 39(2):133-154.
50. Aliev RA, Fazlollahi B, Vahidov R. Genetic algorithms-based fuzzy regression analysis. Soft Comput -A Fusion Found Method Appl 2002, 6(6):470-475.
51. Rosin PL, Ioannidis E. Evaluation of global image thresholding for change detection. Pattern Recogn Lett 2003, 24:2345-2356.
52. Mitchell T. Machine learning and data mining. Commun ACM 1999, 42(11):31-36.
53. Dehuri S, Cho S-B., eds. Knowledge Mining Using Intelligent Agents. London: Imperial College Press, 2010.
54. Lim CP, Jain LC, Dehuri S., eds. Innovations in Swarm Intelligence. Heidelberg, Germany: Springer-Verlag, 2009.
55. Fogel DB. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. Pisacataway, NJ: IEEE Press, 2006.
56. Michalewicz Z. Genetic Algorithms+ Data Structure= Evolution Programs. Berlin, Germany: Springer-Verlag, 1999.
57. Bayes HG. The Theory of Evolution Strategies. Berlin, Germany: Springer-Verlag, 2001.
58. Bayer HG, Schwefel H-R. Evolution strategies: a comprehensive introduction. J Natural Comput 2002, 1(1):3-52.
59. Eiben AE, Smith JE. Introduction to Evolutionary Computing. Berlin, Germany: Springer-Verlag, 2007.
60. Fogel LJ. Intelligence through Simulated Evolution: Forty Years of Evolutionary Programming. New York: John Wiley & Sons, 1999.
61. Koza JR. Genetic Programming: On the Programming of Computers by means of Natural Selection. Cambridge, MA: MIT Press, 1992.
62. Wong ML, Leung KS. Data Mining Using Grammar Based Genetic Programming and Applications. Amsterdam, the Netherlands: Kluwer Academic Publishers, 2000.
63. Larranaga P, Lozano JA. Estimation Distribution Algorithms: A New Tool for Evolutionary Computation. Amsterdam, the Netherlands: Kluwer Academic Publisher, 2001.
64. Baluja S. Population based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163, Carnegie Mellon University, 1994.
65. Harik GR, Lobo FG, Goldberg DE. The compact genetic algorithm. IEEE Trans Evol Comput 1999, 3(4):287-297.
66. Deb K. Multi-Objective Optimization Using Evolutionary Algorithms. New York: John Wiley and Sons, 2001.
67. Schaffer JD. Multiple objective optimization with vector evaluated genetic algorithms. In ICGA 85, 1985, 93-100.
68. Laumanns M, Rudolph G, Schwefel HP. A spatial predator-prey approach to multi-objective optimization. Parallel Problem Solving Nature 1998, 5:241-249.
69. Ziztler E, Thiele L. Multi-objective evolutionary algorithms: a comparative case study and strength pareto approach. IEEE Trans Evol Comput 1999, 3:257-271.
70. Deb K, Agrawal S, Pratap A, Meyarivan T. A fast and elitist multi-objective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 2002, 6(2):182-197.
71. Zitzler E, Thiele L. An evolutionary algorithms for multi-objective optimization: the strength pareto approach. TIK Report 43, Swiss Federal Institute of Technology (ETZ), Zurich, Swizerland, 1998.
72. Zitzler E, Laumanns M, Thiele L. SPEA2: improving the strength pareto evolutionary algorithm for multi-objective optimization. In: Zitzler E, Glannakoglou KC, Tsahalis D, Periaux J, Papailiou K, Fogarty T, eds. Evolutionary Methods for Design, Optimization and Control with Application to Industrial Problems. Barcelona, Spain: International Center for Numerical Methods in Engineering (CIMNE), 2002, 95-100.
73. Klir GJ, Yuan B. Fuzzy Sets and Fuzzy Logic: Theory and Applications. New Delhi, India: Prentice Hall India, 1995.
74. Shi Y, Eberhart R, Chen Y. Implementation of evolutionary fuzzy systems. IEEE Trans Fuzzy Syst 1999, 7(2):109-119.
75. Kohavi R, John GH. Wrappers for feature subset selection, Artif Intell 1997, 97(1-2):273-324.
76. Langley P. Selection of relevant features in machine learning. In: Proceedings of AAAI Fall Symposium on Relevance, 1994, 1-5.
77. Liu H, Motoda H, eds. Selecting Features by Vertical Compactness of Data, Amsterdam, the Netherlands: Kluwer, 1998.
78. Piramuthu S. Evaluating feature selection methods for learning in data mining applications. In: Proceedings of 31st Annual Hawaii International Conference on System Science, 1998.
79. Martin-Bautista MJ, Vila M-A . A survey of genetic feature selection in mining issues. In: Proceedings of 1999 Congress on Evolutionary Computation (CEC'99), 1999, 1314-1321.
80. Messer K, Kittler J. Using feature selection to aid an iconic search through an image database. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1997, 1605-2608.
81. Liu Y, Dellaert F. A classification based similarity metric for 3Dimage retrieval. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 1998, 800-805.
82. Puuronen S, Tsymbal A, Skrypnik I. Advanced local feature selection in medical diagnostics. In: Proceedings of 13th IEEE Symposium on Computer-Based Medical Systems, 2000, 25-30.
83. Ishikawa H. Multiscale feature selection in stereo. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 1999, 132-137.
84. Oh I-S, Lee JS, Suen CY. Analysis of class separation and combination of class dependent features for handwriting recognition, IEEE Trans Pattern Anal Mach Intell 1999, 21(10):1089-1094.
85. Liu H, Motoda H. Feature Selection for Knowledge Discovery and Data Mining. Norwell, MA: Kluwer, 1998.
86. Huang J, Cai Y, Xu X. A filter approach to feature selection based on mutual information. In: Proceedings of 5th IEEE International Conference on Cognitive Informatics, 2006, 84-89.
87. Sanchez-Marona N, Alonso-Betanzos A, Tombilla-Sanroman M. Filter Methods for Feature Selection-A Comparative Study. In Yin H, Tino P, Corchado E, Byrne W, Yao X, eds. Intelligent Data engineering and Automated Learning (IDEAL 2007), LNCS, vol. 4881. Berlin Heidelberg: Springer-Verlag, 2007, 178-187.
88. Cherkauer KJ, Shavlik JW. @rowing simpler decision trees to facilitate knowledge discovery. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD'96). Menlo Park, CA: AAAI Press, 1996, 315-318.
89. Bala J. Using learning to facilitate the evolution of features for recognizing visual concepts. Evol Comput 1996, 4(3):297-312.
90. Bala J, Huang J, Vafaie H, Dejong K, Wechsler H. Hybrid learning using genetic algorithms and decision trees for pattern classification. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'95). Montréal Québec, Canada: Morgan Kaufmann, 1995, 719-724.
91. Chen S, Guerra-Salcedo C, Smith S. Nonstandard crossover for a standard representation- commonality based feature subset selection. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'99). Orlando, FL: Morgan Kaufmann, 1999, 129-134.
92. Guerra-Salcedo C. Genetic search for feature subset selection: A comparison between CHC and GENESIS. In: Genetic Programming 1998: Proceedings of the Third Annual Conference, 1999, 504-509.
93. Liu H, Motoda H., eds. Feature Subset Selection Using a Genetic Algorithm. Norwell, MA: Kluwer, 1998.
94. Moser A, Murty MN. On the scalability of genetic algorithms to very large scale feature selection. In: Proceedings of the Real World applications of Evolutionary Computation (EvoWorkshops 2000), LNCS 1803. Berlin, Germany: Springer-Verlag, 2000, 77-86.
95. Ishibuchi H, Nakashima T. Multi-objective pattern and feature selection by a genetic algorithm. In Proceedings of the 2000 Genetic and Evolutionary Computation Conference (GECCO' 2000), 2000, 1069-1076.
96. Guerra-Salcedo C, Whitley D. Feature Selection Mechanisms for Ensemble Creation: A Genetic Search Perspective. Technical Report WS-99-06, Menlo Park, CA:AAAI Press, 1999, 13-17.
97. Derrac J, Garcia S, Herrera F. IFS-CoCo: instance and feature selection based on cooperative coevolution with nearest neighbor rule. Pattern Recogn 2010, 43(6):2082-2105.
98. Huang Y, Cai J, Xu X. A hybrid genetic algorithm for feature selection wrapper based on mutual information. Pattern Recogn Lett 2007, 28:1825-1844.
99. Kabir MM, Shahjahan M, Murase K. Involving new local search in hybrid genetic algorithm for feature selection. Lect Notes in Comput Sci 2009, 5864: 150-158.
100. Li Y, Zeng X. Sequential multi-criteria feature selection algorithm based on agent genetic algorithm. Appl Intell 2010, 33(2):117-131.
101. Lim TS, Loh W-Y, Shih Y-S. A comparison of prediction accuracy, complexity and training time of thirty three old and new classification algorithms. Mach Learn J 2000, 40:203-228.
102. Michie D, Spiegelhalter DJ, Taylor CC. Machine Learning, Neural and Statistical Classification. New York: Ellis Horwood, 1994.
103. Katsikopoulos KV, Fasolo B. New tools for decision analysts, IEEE Trans Syst Man Cybern A 2006, 36(5):960-967.
104. Quinlan JR. Induction of decision trees. J Mach Learn 1986, 1(1):81-106.
105. Utgoff PE. Incremental induction of decision trees. J Mach Learn 1989, 4:161-186.
106. Utgoff PE. ID5: An incremental ID3. In: Proceedings of the Fifth International Conference on Machine Learning. San Mateo, CA: Morgan Kaufmann, 1988, 107-120.
107. Utgoff PE. An improved algorithm for incremental induction of decision trees. In: Proceedings of 11th International Conference on Machine Learning.1994, 318-325.
108. Quinlan JR. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
109. Loh W-Y. Classification and Regression Trees, WIREs Data Mining Knowl Discov 2011, 1:14-23.
110. Dehuri S, Mall R. Predictive and comprehensible rule discovery using a multi-objective genetic algorithms. Knowl-Based Syst 2006, 19:413-421.
111. Dhar V, Chou D, Provost F. Discovering interesting patterns for investment decision making with GLOWER: a genetic learner overlaid with entropy reduction. Data Mining Knowl Discov J 2000, 4(4):251-280.
112. Hekanaho J. Testing different sharing methods in concept learning. TUCS Technical Report, 71, Center for Computer Science, Finland, 1996.
113. Weiss GM. Timeweaver: a genetic algorithm for identifying predictive patterns in sequences of events. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'99). San Mateo, CA: Morgan Kaufmann, 1999, 718-725.
114. Freitas AA. A survey of evolutionary algorithms for data mining and knowledge discovery. In Ghosh A, Tsutsui S, eds. Advances in Evolutionary Computation. Berlin, Germany: Springer-Verlag, 2002, 819-845.
115. Janikow CZ. A knowledge intensive genetic algorithm for supervised learning, Mach Learn 1993, 13:189-228.
116. Hekanaho J. Symbiosis in multi-modal concept learning. In: Proceedings of the 1995 International Conference on Machine Learning (ICML' 96). San Mateo, CA: Morgan Kaufmann, 1996, 234-242.
117. Giordana A, Neri F. Search intensive concept induction. Evol Comput 1995, 3(4):375-416.
118. Mansilla EB, Mekaouche A, Guiu JMG. A study of genetic classifier system based on the Pittsburg approach on a medical domain. In: Proceedings of the 12th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE'99). LNCS, 1611. Berlin, Germany: Springer-Verlag, 1999, 175-184.
119. Kwedlo W, Kretowski M. An evolutionary algorithm using multivariate discritization for decision rule induction. In: Proceedings of the 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD' 99). LNCS 1704, Berlin, Germany: Springer-Verlag, 1999, 392-397.
120. Dehuri S, Ghosh A, Mall R. Genetic algorithms for multi-criterion classification and clustering in data mining. Int J Comput Inform Sci 2006, 4(3):143-154.
121. Dehuri S, Patnaik S, Ghosh A, Mall R. Application of elitist multi-objective genetic algorithm for classification rule generation, Appl Soft Comput 2008, 8:477-487.
122. Hekanaho J. Background knowledge in ga based concept learning. In: Proceedings of the 13th International Conference on Machine Learning (ICML'96), 1996, 234-242.
123. Pei M, Goodman ED, Punch WF. Pattern discovery from data using genetic algorithms. In: Proceedings of the 1st Pacific Asia Conference on Knowledge Discovery and Data Mining, 1997.
124. Banzhaf W. Interactive evolution. In Back T, Fogel DB, Michalewicz T, eds. Evolutionary Computation 1. London: Institute of Physics Publishing, 2000, 132-135.
125. Weiss SM, Kulikowski CA. Computer Systems that Learn. San Mateo, CA: Morgan Kaufmann, 1991.
126. Thomas JD. Sycara K. In: Freitas AA, ed. Data Mining with Evolutionary Algorithms: Research Directions-Papers from the AAAI'99/GECCO'99 Workshop. Technical Report WS-99-06, Palto Alto, CA: AAAI Press, 1999, 7-11.
127. Romao W, Freitas AA, Pacheco RCS. A genetic algorithm for discovering interesting fuzzy prediction rules: applications to science and technology data. In: Proceedings of the 2002 Genetic and Evolutionary Computation Conference (GECCO' 2002). New York: Morgan Kaufmann, 2002, 1188-1195.
128. Noda E, Freitas AA, Lopes HS. Discovering interesting prediction rules with a genetic algorithm. In: Proceedings of the Conference on Evolutionary Computation-1999 (CEC' 99). Washington D. C.: IEEE Press, 1999, 1322-1329.
129. Syswerda G. Uniform crossover in genetic algorithms. In: Proceedings of the 3rd International Conference on Genetic Algorithms. San Francisco, CA: Morgan Kaufmann, 1989, 2-9.
130. Wang L, Yen J. Extracting fuzzy rules for system modelling using a hybrid of genetic algorithms and kalman filter. Fuzzy Sets Syst 1999, 101:353-362.
131. Castro JL, Castro-Schez JJ. Zurita J. Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert system. Fuzzy Sets Syst 1999, 101:331-342.
132. Chang C, Chen S. Constructing membership functions and generating weighted fuzzy rules from training data. In: Proceedings of the Ninth National Conference on Fuzzy theory and Its Applications, 2001, 708-713.
133. Hong T, Lee C. Introduction of fuzzy rules and membership functions from training examples. Fuzzy Sets Syst 1996, 84:33-47.
134. Wu TP, Chen SM, A new method for constructing membership functions and fuzzy rules from training examples. IEEE Trans Syst Man Cybernetics B 1999, 29:25-40.
135. Wong C-C, Chen C-C. A GA-based method for constructing fuzzy systems directly from numerical data. IEEE Trans Syste Man Cybern B 2002, 30:904-911.
136. Roubos J, Setnes M, Abony J. Learning fuzzy classification rules from labeled data. Inform Sci 2003, 150(1-2):77-93.
137. Setnes M, Roubos J. GA-fuzzy modelling and classification: complexity and performance. IEEE Trans Fuzzy Syst 2000, 8(5):509-522.
138. Ishibuchi H, Nozaki K, Tanaka H. Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets Syst 1992, 52(1):21-32.
139. Ishibuchi H, Nozaki K, Yamamoto N, Tanaka H, 1994. Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms, Fuzzy Sets Syst 65(2/3): 237-253.
140. Gonzalez A, Perez R. SLAVE: a genetic learning system based on iterative approach. IEEE Trans Fuzzy Syst 1999, 7(2):176-191.
141. Hu Y, Chen R, Tzeng G. Finding fuzzy classification rules using data mining technique. Pattern Recogn Lett 2003, 24:509-519.
142. Ho SY, Chen H, Ho SJ, Chen T. Design of accurate classifiers with compact fuzzy-rule base using evolutionary scatter partition of feature space. IEEE Trans Syst Man Cybernet B 2004, 34(2):1031-1044.
143. Chen S, Tsai F. Generating fuzzy rules from training instances for fuzzy classification systems. Expert Syst Appl 2008, 35(3):611-621.
144. Chen Y, Wang L, Chen S. Generating weighted fuzzy rules from training data for dealing with the iris data classification problem. Int J Appl Sci Eng 2006, 4(1):41-52.
145. Abe S, Lan M. A method for fuzzy rules extraction directly from numerical data and application to pattern classification. IEEE Trans Fuzzy Syst 1995, 3(1):18-28.
146. Mitra S, Kuncheva LI. Improving classification performance using fuzzy MLP and two-level selective partitioning of feature space. Fuzzy Sets Syst 1995, 70(1):1-13.
147. Nauck D, Kruse R. A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets Syst 1997, 89(3):227-288.
148. Uebele V, Abe S, Lan MS. A neural-network-based fuzzy classifier, IEEE Trans Systems, Man Cybernetics 1995, 25(2):353-361.
149. Chokrobotry U, Pal NR. A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification. IEEE Trans Neural Netw 2004, 15(1):110-123.
150. Abe S, Thawonmas R. A fuzzy classifier with ellipsoidal regions. IEEE Trans Fuzzy Syst 1997, 5(3):358-368.
151. Chung Hu Y, Hshiung Tzeng G. Elicitation of classification rules by fuzzy data mining. Appl Artif Intell 2003, 16(7-8):709-716.
152. DeCock M, Cornelis C, Kerre EE. Elicitation of fuzzy association rules from positive and negative examples. Fuzzy Sets Syst 2003, 149(1):73-85.
153. Ishibuchi H, Yamamoto T. Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining, Fuzzy Sets Syst 2004, 141(1):59-88.
154. Ishibuchi H, Nakashima T, Murata T. Three-objective genetic-based machine learning for linguistic rule extraction. Inform Sci 2001, 134(1-4):109-133.
155. Chen SM, Chen Y. Automatically constructing membership functions and generating fuzzy rules using genetic algorithms. Cybern Syst 2002, 33(8):841-862.
156. Cordon O, Gomide F, Herrera F, Hoffmann F, Magdalena L. Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst 2004, 41:5-31.
157. Zhou E, Khotanzad A. Fuzzy classifier design using genetic algorithms. Pattern Recogn 2007, 40:3401-3414.
158. Saniee Adadeh M, Habibi J, Lucas C. Intrusion detection using a fuzzy genetics-based learning algorithm. J Netw Comput Appl 2007, 30:414-428.
159. Aguilera JJ, Chica M, del Jesus MJ. Herrera F, Niching genetic feature selection algorithms applied to design of fuzzy rule-based classification systems. In: IEEE International Fuzzy System Conference, FUZZ-(IEEE'07), 2007, 1-6.
160. Monsoori E, Zolghadri M, Katebi S. SGERD, a steady-state genetic algorithm for extracting fuzzy classification rules from data. IEEE Trans Fuzzy Syst 2008, 16(4):1061-1072.
161. Karr CL. Design of an adaptive fuzzy logic controller using a genetic algorithms. In: Proceedings of Fourth International Conference on Genetic Algorithms, 1991, 450-457.
162. Karr CL, Gentry EJ. Fuzzy controller of ph using genetic algorithms, IEEE Trans Fuzzy Syst 1993, 1(1):46-53.
163. Bentley PJ. Evolutionary my dear Watson-investigating committee based evolution of fuzzy rules for the detection of suspicious insurance claims. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO' 2000). San Mateo, CA: Morgan Kaufmann, 2000, 702-709.
164. Fertig CS, Freitas AA, Arruda LVR, Kaestner C. A fuzzy beam search rule induction algorithm. In: Principles of data mining and knowledge discovery (Proceedings of 3rd European Conference-PKDD'99). LNAI 1704, Berlin, Germany: Springer-Verlag, 1999, 341-347.
165. Walter D, Mohan CK ClaDia: a fuzzy classifier system for disease diagnosis. In : Proceedings of the Congress on Evolutionary Computation (CEC'2000), 2000, 2:1429-1435.
166. Crockett KA, Bandar Z, Al Attar A. Soft decision trees: a new approach using non-linear fuzzification. In: Proceedings of the 9th IEEE International Conference Fuzzy Systems (FUZZ IEEE' 2000), 2000, 209-215.
167. Mota C, Ferreira H, Rosa A. Independent and simultaneous evolution of fuzzy sleep classifiers by genetic algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO' 1999). San Mateo, CA: Morgan Kaufmann, 1999, 1622-1629.
168. Chen H-M, Ho S-Y. Designing an optimal evolutionary fuzzy decision tree for data mining. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO' 2001). San Mateo, CA: Morgan Kaufmann, 2001, 943-950.
169. Mendes W, Romao RF, Freitas AA, Pacheco RCS. Discovering fuzzy classification rules with genetic programming and co-evolution. In Principles of data mining and knowledge discovery (Proceedings of the 5th European Conference, PKDD'2001). LNAI 2168, Berlin, Germany: Springer-Verlag, 2001, 314-325.
170. Herrera F. Genetic fuzzy systems: taxonomy current research trends and prospects. Evol Intell 2008, 1:27-46.
171. Castellano G, Fanelli A, Gentile E, Roselli T. A GA-based approach to optimisation of fuzzy models learned from data. In:+ GECCO Program, New York, 2002, 5-8.
172. Guillaume S. Designing fuzzy inference systems from data: An interpretability oriented review. IEEE Trans Fuzzy Syst 2001, 9(3):426-443.
173. Jimenez F, Gomez-Skarmeta AF, Roubos H, Babuska R. Accurate, transparent and compact fuzzy models for function approximation and dynamic modelling through multiobjective evolutionary optimisation. In: First International Conference on Evolutionary Multi-criterion Optimisation, 2001, 653-667.
174. Jin Y, Vonseelen W, Sendhoff B. An approach to rule-base knowledge extraction. In: Proceedings of IEEE Conference on Fuzzy System, 1998, 1188-1193.
175. Jin Y, Vonseelen W, Sendhoff B. On generating FC3 fuzzy rule systems from data using evolution strategies. IEEE Trans Syst Man Cybern 1999, 29(6):829-845.
176. Jin Y, Sendhoff B. Extracting interpretable fuzzy rules from RBF networks. Neural Process Lett 2003, 17(2):149-164.
177. Roubos H, Setnes M. GA-fuzzy modelling and classification: Complexity and performance. IEEE Trans Fuzzy Syst 2000, 8(5):509-522.
178. Rojas I, Pomares H, Ortega J, Prieto A. Self-organised fuzzy system generation from training examples. IEEE Trans Fuzzy Syst 2000, 8(1):23-36.
179. Casillas J, Cordon O, Herrera F, Magdalena L. Accuracy Improvements in Linguistic Fuzzy Modelling. Berlin, Germany: Springer-Verlag, 2003.
180. Alcala R, Alcala Fdez J, Casillas J, Cordon O, Herrera F. Hybrid learning models to get the interpretability-accuracy trade off in fuzzy modelling. Soft Comput 2006, 10:717-734.
181. Ishibuchi H, Nojima Y. Analysis of interpretability-accuracy trade off of fuzzy systems by multi-objective fuzzy genetic-based machine learning. Int J Appprox Reason 2007, 44(1):4-31.
182. Cordon O. A historical review of evolutionary learning methods for mamdani-type fuzzy rule-based systems: designing interpretable genetic fuzzy systems. Int J Approx Reason 2011, 52(6):894-913.
183. Jin Y. Fuzzy modelling of high-dimensional systems: Complexity reduction and interpretability improvements. IEEE Trans Fuzzy Syst 2000, 8(2):212-221.
184. Nauck DD. Fuzzy data analysis with NEFCLASS. Approx Reason 2003, 32:103-130.
185. Cordon O, Herrera F, Zwir I. A proposal for improving the accuracy of linguistic modelling. IEEE Trans Fuzzy Syst 2000, 8(3):335-344.
186. Mikut R, Jakel J, Groll L. Interpretability issues in data-based learning of fuzzy systems. Fuzzy Sets and Systems 2005, 150:179-197.
187. Oliveria JVD. Semantic constraints for membership function optimisation. IEEE Trans. on Systems, Man and Cybernetics: Part-A Systems and Humans 1999, 29(1):128-138.
188. Gacto MJ, Alcala R, Herrera F. Interpretability of linguistic fuzzy rule based systems: an overview of interpretability measures. Information Science 2011, 181(20):4340-4360.
189. Gacto MJ, Alcala R, Herrera F. Integration of an index to preserve the semantic interpretability in the multi-objective evolutionary rule selection and tuning of linguistic fuzzy systems. IEEE Transactions on Fuzzy Systems 2010, 18:515-531.
190. Zhou SM, Gan JQ. Low level interpretability and high level interpretability a unified view of data driven interpretable fuzzy system modeling. Fuzzy Sets and Systems 2008, 159:3091-3131.
191. Ishibuchi H, Nozaki K, Yamamoto N, Tanaka H. Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Transactions on Fuzzy Systems 1995, 3(3):260-270.
192. Ishibuchi H, Murata T. A multi-objective genetic based local search algorithm and its application to flowshop scheduling. IEEE Tran Syst Man Cybernet C 1998, 28:392-403.
193. Ishibuchi H, Murata T, Turksen I. Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets Syst 1997, 89(2):135-149.
194. Ishibuchi H, Doi T, Nojima Y. Incorporation of Scalarizing Fitness Functions into Evolutionary Multi-objective Optimization Algorithms, Vol. 4193, PPSN IX. Berlin, Germany: Springer-Verlag, 2006.
195. Isibuchi H, Nojima Y, Kuwajima I. Fuzzy data mining by heuristic rule extraction and multiobjective rule selection. In: 2006 IEEE International Conference on Fuzzy Systems, Canada2006, 1633-1640.
196. Chen J-L, Yuan-Long H, Zong-Yi X, Li-Min J, Zhong-Zhi T. A multi-objective genetic-based method for design fuzzy classification system, Int J Comput Sc Netw Secutity 2006, 6(8 A):110-117.
197. Alcala R, Gacto M, Herrera F. A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic rule-based system. Int J Uncertain, Fuzziness Knowled-Based Syst 2007, 15(5):539-557.
198. Eshelman LJ, Schaffer JD. Real-coded genetic algorithms and interval schemata. Found Genetic Algorithms 1993, 2:187-202.
199. Eshelman LJ, Schaffer JD. The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. Foundat Genetic Algorithms 1991, 1:265-283.
200. Gacto MJ, Alcala R, Herrera F. Adaptation and application of multi-objective evolutionary algorithm for rule selection and parameter tuning of fuzzy rule-based systems. Soft Comput 2009, 13:419-436.
201. Pulkkinen P, Koivisto H. Fuzzy classifier identification using decision tree and multi-objective evolutionary algorithms. Int J Approx Reason 2008, 48:526-283.
202. Pulkkinen P. A multi-objective genetic fuzzy system for obtaining compact and accurate fuzzy classifiers with transparent fuzzy partitions. In: Proceedings of 8th International Conference on Machine Learning and Applications. Miami Beach, FL: IEEE Press, 2009, 84-94.
203. Marquez A, Marquez F, Peregrin A. A multi-objective evolutionary algorithm with an interpretability improvement mechanism for linguistic fuzzy systems with adaptive defuzzification. In: IEEE World Congress on Computational Intelligence, 2010, 277-283.
204. Mishra BSP, Dehuri S, Mall R, Ghosh A. Parallel single and multi-objective genetic algorithms: a survey. Int J Appl Evol Comput 2011, 2(2):21-58.
205. Mishra BSP, Addy AK, Roy R, Dehuri S. Parallel multi-objective genetic algorithms for associative classification rule mining. In: Proceedings of International Conference on Communication Computing and Security. New York: ACM Press, 2011.
206. Pappa GL, Freitas AA. Automating the Design of Data Mining Algorithms: an Evolutionary Computation Approach. Berlin, Germany: Springer-Verlag, 2010.
207. Muni DP, Pal NR, Das J. Genetic programming for simultaneous feature selection and classifier design. IEEE Trans Syst Man Cybernet B 2006, 36(1):106-117.
208. Folino G, Pizzuti C, Spezzano G. Gp ensembles for large scale data classification. IEEE Trans Evol Comput 2006, 10(5):604-616.
209. Kennedy J, Eberhart RC. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, 1995, 1942-1948.
210. Clerc M, Kennedy J. The particle swarm explosion, stability, convergence in a multi-dimensional complex space. IEEE Trans Evol Comput 2002, 6(1):68-73.
211. Dehuri S, Cho S-B. Multi-criterion pareto based particle swarm optimized polynomial neural network for classification: a review and state-of-the-art, Comput Sci Rev 2009, 3(1):19-40.
212. Dorigo M, Stutzle T. Ant colony optimization. Cambridge, MA: MIT Press, 2004.
213. Parpinelli RS, Lopes HS, Freitas AA. Data mining with an ant colony optimization algorithm. IEEE Trans Evol Computat 2002, 6(4):321-332.
214. Martens D, Backer MD, Haesen R, Vanthienen J, Snoeck M, Baesens B. Classification with ant colony optimization. IEEE Trans Evol Comput 2007, 11(5):651-665.
215. Fathian M, Amiri B, Maroosi A. Application of honey bee mating optimization algorithm on clustering. Appl Math Comput 2007, 190:1502-1513.
216. Karaboga D, Basturk B, Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. Lect Notes Artifi Intell 2007, 4529:789-798.
217. Karaboga D, Basturk B. On the performance of artificial bee colony (abc) algorithm. Appl Soft Comput 2008, 8:687-697.