Contextual Fuzzy-Based Decision Support System Through Opinion Analysis: A Case Study at University of the Salerno

International Journal of Information Technology and Decision Making

Volumn 15, Issue 5, 2016, Pages 923-948

  De Maio, Carmen ^a   Tommasetti, Aurelio ^a   Troisi, Orlando ^a   Vesci, Massimiliano ^a   Fenza, Giuseppe ^a   Loia, Vincenzo ^a  

^a UNIVERSITY OF SALERNO   (Italy)

Author keywords

commitment; description logic; DSS; Fuzzy logic; loyalty; ontology

Indexed keywords

EID: 84979074071     PISSN: 02196220     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219622016500231     Document Type: Article

References (70)

1. V. A. Lotkowski, S. B. Robbins and R. J. Noeth, The role of academic and non-academic factors in improving college retention, Act Policy Report, American College Testing ACT Inc., 2004.
2. Ø. Helgesen and E. Nesset, What accounts for students' loyalty? Some field study evidence, International Journal of Educational Management 21(2) (2007) 126-143.
3. A. Gustafsson, M. D. Johnson and I. Roos, The effects of customer satisfaction, relationship commitment dimensions, and triggers on customer retention, Journal of Marketing 69(4) (2005) 210-218.
4. Y.-C. Ho and C.-T. Tsai, Comparing ANFIS and SEM in linear and nonlinear forecasting of new product development performance, Expert Systems with Applications 38(6) (2011) 6498-6507.
5. A. Aktepe, S. Ersz and B. Toklu, Customer satisfaction and loyalty analysis with classification algorithms and structural equation modeling, Computers & Industrial Engineering 86 (2014) 95-106.
6. E. Turban, J. Aronson and T. Liang, Decision Support Systems and Intelligent Systems (Pearson/Prentice Hall, New Jersey 2005).
7. Z. Guangquan, X. Yang and L. Tianrui, A special issue on new trends in Intelligent Decision Support Systems, Knowledge-Based Systems 32 (2012) 1-2.
8. S. Suresh, M. Naidu and S. A. Kiran, An xml based knowledge-driven decision support system for design pattern selection, International Journal of Research in Engineering and Technology (IJRET) 1(3) (2012) 116-124.
9. K. Nakamura, S. Iwai and T. Sawaragi, Decision support using causation knowledge base, in systems, IEEE Transactions on Man and Cybernetics 12(6) (1982) 765-777, doi: 10.1109/TSMC.1982.4308910.
10. P. Sherimon, P. Vinu, R. Krishnan and Y. Takroni, Risk assessment prediction of hypertension and its associated diseases-an ontology driven model, in Int. Conf. Computer Medical Applications (ICCMA), 2013 (2013), pp. 1-3.
11. M. Grond, J. Morren and H. Slootweg, Requirements for advanced decision support tools in future distribution network planning, in 22nd Int. Conf. Exhibition on Electricity Distribution (CIRED 2013), (2013), pp. 1-4.
12. X. Yingzhuo and W. Liupeng, Research of visualization intelligent decision support system for drilling risk control, in Chinese Control and Decision Conference (CCDC, 2010) (2010), pp. 3822-3827.
13. I. Bose and X. Chen, Detecting the migration of customers of mobile services using fuzzy clustering, Information & Management 52(2) (2015) 227-238.
14. V. Adriaenssens, B. D. Baets, P. L. Goethals and N. D. Pauw, Fuzzy rule-based models for decision support in ecosystem management, Science of The Total Environment 319(13) (2004) 1-12.
15. G. Fenza, D. Furno, V. Loia and S. Senatore, Approximate Processing in Medical Diagnosis by means of Deductive Agents, In Handbook on Reasoning-Based Intelligent Systems, ISBN: 9789814329477, pp. 633-657. DOI: 10.1142/9789814329484 0023.
16. A. Hegazy and M. El-Battah, M. Kadry, Fuzzy-based framework for enterprise resource planning system selection, in 22nd Int. Conf. Computer Theory and Applications (ICCTA), 2012 (2012), pp. 139-147.
17. F. J. Cabrerizo, S. Alonso and E. Herrera-Viedma, A consensus model for group decision making problems with unbalanced fuzzy linguistic information, International Journal of Information Technology & Decision Making 8(1) (2009) 109-131.
18. C. D. Maio, G. Fenza, M. Gaeta, V. Loia and F. Orciuoli, A knowledge-based framework for emergency fDSSg, Knowledge-Based Systems 24(8) (2011) 1372-1379.
19. T. Entani, Process of group decision making from group and individual viewpoints, in Soft Computing and Intelligent Systems (SCIS) and 13th Int. Symp. Advanced Intelligent Systems (ISIS), 2012 Joint 6th Int. Conf., (2012), pp. 1322-1326. DOI: 10.1109/SCISISIS. 2012.6505055.
20. S. Alonso, E. Herrera-Viedma, F. Chiclana and F. Herrera, Individual and social strategies to deal with ignorance situations in multi-person decision making, International Journal of Information Technology & Decision Making 8(2) (2009) 313-333.
21. T. Hennig-Thurau, M. F. Langer and U. Hansen, Modeling and managing student loyalty an approach based on the concept of relationship quality, Journal of Service Research 3(4) (2001) 331-344.
22. M. G. Gallarza and I. G. Saura, Value dimensions, perceived value, satisfaction and loyalty: An investigation of university students travel behavior, Tourism Management 27 (2006) 437-452.
23. R. R. Brown and T. M. Mazzarol, The importance of institutional image to student satisfaction and loyalty within higher education, Higher Education 58(1) (2009) 81-95.
24. G. Ribes Giner and A. Peralt Rillo, Structural equation modeling of co-creation and its influence on the student's satisfaction and loyalty towards university, Journal of Computational and Applied Mathematics 291 (2016) 257-263.
25. Ø. Helgesen and E. Nesset, Images, satisfaction and antecedents: Drivers of student loyalty? a case study of a norwegian university college, Corporate Reputation Review 10(1) (2007) 38-59.
26. K. Sang-Bum, H. Kyoung-Soo, R. Hae-Chang and M. Sung Hyon, Some Effective Techniques for Naive Bayes Text Classification, IEEE Transactions on Knowledge and Data Engineering 18(11) (2006) 1457-1466.
27. D. Shaw and J. S. Edwards, Building user commitment to implementing a knowledge management strategy, Information & Management 42(7) (2005) 977-988.
28. S. W. Chou, A. A. Techatassanasoontorn and I. H. Hung, Understanding commitment in business process outsourcing relationships, Information & Management 52(1) (2015) 30-43.
29. I. Geyskens, J.-B. E. Steenkamp, L. K. Scheer and N. Kumar, The effects of trust and interdependence on relationship commitment: A trans-atlantic study, International Journal of Research in Marketing 13(4) (1996) 303-317.
30. N. J. Allen and J. P. Meyer, The measurement and antecedents of affective, continuance and normative commitment to the organization, Journal of Occupational Psychology 63 (1990) 1-18.
31. I. Feikov, An index method for measurement of customer satisfaction, The TQM Magazine 16(1) (2004) 57-66.
32. H. Lin, W. Fan and P. Y. Chau, Determinants of user continuance of social networking sites: A self-regulation perspective, Information & Management 51(5) (2014) 595-603.
33. R. Oliver, Satisfaction: A Behavioral Perspective on the Consumer, McGraw-Hill series in marketing (McGraw Hill, US, 1997).
34. R. L. Oliver and J. E. Swan, Consumer perceptions of interpersonal equity and satisfaction in transactions: A field survey approach, Journal of Marketing 53(2) (1989) 21-35.
35. O. W. De Shields, A. Kara and E. Kaynak, Determinants of business student satisfaction and retention in higher education: Applying herzberg's two-factor theory, International Journal of Educational Management 19(2) (2005) 128-139.
36. A. H. Zins, Relative attitudes and commitment in customer loyalty models, International Journal of Service Industry Management 12(3) (2001) 269-294.
37. D. D. Gremlera and S. W. Brownb, Service Loyalty: Its Nature, Importance, and Implications, in Advancing Service Quality: A Global Perspective, B. Edvardsson, S. Brown and R. Johnston (eds.) (International Service Quality Association, 1996 Jamaica, NY). pp. 171-80.
38. M. P. Pritchard and D. R. Howard, The loyal traveler: Examining a typology of service patronage, Journal of Travel Research 35(4) (1997) 2-10.
39. A. S. Mattila, How affective commitment boosts guest loyalty (and promotes frequentguest programs), Cornell Hotel and Restaurant Administration Quarterly 47(2) (2006) 174-181.
40. H. Evanschitzky, G. R. Iyer, H. Plassmann, J. Niessing and H. Meffert, The relative strength of affective commitment in securing loyalty in service relationships, Journal of Business Research 59(12) (2006) 1207-1213.
41. T. W. Andreassen and B. Lindestad, Customer loyalty and complex services: The impact of corporate image on quality, customer satisfaction and loyalty for customers with varying degrees of service expertise, International Journal of Service Industry Management 9(1) (1998) 7-23.
42. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Kluwer Academic Publishers, US, 1981).
43. H.-C. Yu, Z.-Y. Lee and S.-C. Chang, Using a fuzzy multi-criteria decision making approach to evaluate alternative licensing mechanisms, Information & Management 42(4) (2005) 517-531.
44. X. L. Xie and G. Beni, A validity measure for fuzzy clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence 13(8) (1991) 841-847.
45. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Springer Science & Business Media, Germany 2013).
46. R. N. Dave, Validating fuzzy partitions obtained through c-shells clustering, Pattern Recognition Letters 17(6) (1996) 613-623.
47. I. Gath and B. G. Amir, Unsupervised optimal fuzzy clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7) (1989) 773-780.
48. N. R. Pal and C. B. James, On cluster validity for the fuzzy c-means model, IEEE Transactions on Fuzzy Systems 3(3) (1995) 370-379.
49. M. Alata, M. Mohammad and R. Abdullah, Optimizing of fuzzy c-means clustering algorithm using GA, Update 1.5 (2008).
50. S. L. Chiu, Fuzzy model identification based on cluster estimation, Journal of Intelligent & Fuzzy Systems 2(3) (1994) 267-278.
51. R. R. Yager and P. F. Dimitar, Generation of fuzzy rules by mountain clustering, Journal of Intelligent & Fuzzy Systems 2(3) (1994) 209-219.
52. F. Hoppner, F. Klawonn, R. Kruse and T. Runkler, Fuzzy cluster analysis: Methods for classification, data analysis and image recognition, Journal Operational Research Society 51(6) (2000) 769-769.
53. J. Zhao and K. B. Bimal, Evaluation of membership functions for fuzzy logic controlled induction motor drive, IEEE 28th Annual Conference of the IECON 02 Industrial Electronics Society, Vol. 1 (IEEE, 2002).
54. L. Kuncheva, Fuzzy Classifier Design, Vol. 49 (Springer Science & Business Media, Germany 2000).
55. E. H. Mamdani, Application of fuzzy logic to approximate reasoning using linguistic synthesis, IEEE Transactions on Computers C 26(12) (1977) 1182-1191.
56. M. Saltan and T. Serdal, Backcalculation of pavement layer thickness and moduli using adaptive neuro-fuzzy inference system, In Soft Computing in Pavement and Geomechanical Systems: Recent Advances (2009) 177-204.
57. I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques, Morgan Kaufmann, 2005.
58. W. Xi-Zhao, Z. Jun-Hai and L. Shu-Xia, Induction of multiple fuzzy decision trees based on rough set technique, Information Sciences 178(16) (2008) 3188-3202.
59. E. Mikel, G. Mikel and S. Jose, Humberto Bustince, Fuzzy Rule-Based Classification Systems for multi-class problems using binary decomposition strategies: On the influence of n-dimensional overlap functions in the Fuzzy Reasoning Method, Information Sciences 332(1) (2016) 94-114.
60. S. Chung-Kwan, T. Y. Ui, K. K. Huy and C. P. Sang, A hybrid approach of neural network and memory-based learning to data mining, IEEE Transactions on Neural Networks 11(3) (2000) 637-646.
61. W. Yue and I. I. Hussein, Bayesian-Based Decision-Making for Object Search and Classification, IEEE Transactions on Control Systems Technology 19(6) (2011) 1639-1647, doi: 10.1109/TCST.2010.2087760.
62. Y. Hualong, M. Chaoxu, S. Changyin, Y. Wankou, Y. Xibei and Z. Xin, Support vector machine-based optimized decision threshold adjustment strategy for classifying imbalanced data, Knowledge-Based Systems 76 (2015) 67-78.
63. S. L. Salzberg, C4.5: Programs for machine learning by J. Ross Quinlan. Morgan Kaufmann Publishers, inc., 1993, Machine Learning 16 (3) (1994) 235-240.
64. J.-S. R. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics 23(3) (1993) 665-685.
65. R. Caruana and A. Niculescu-Mizil, An empirical comparison of supervised learning algorithms, in: Proc. of the 23rd Int. Conf. Machine Learning (ACM, 2006), pp. 161-168.
66. P. Chih-Hsiang, K. Ta-Wen, L. Po-Chuan, W. Jhing-Fa and W. Guo-Ji, Trainable and Low-Cost SMO Pattern Classifier Implemented via MCMC and SFBS Technologies, IEEE Transactions on Very Large Scale Integration (VLSI) Systems 23(10) (2015) 2295-2306.
67. F. Saki and N. Kehtarnavaz, Background noise classification using random forest tree classifier for cochlear implant applications, in IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP) 2014, pp. 3591-3595, May 4-9, 2014.
68. F. Bobillo and S. Umberto, Optimising fuzzy description logic reasoners with general concept inclusion absorption, Fuzzy Sets and Systems 292 (2016) 98-129.
69. G. Kou, Y. Lu, Y. Peng and Y. Shi, Evaluation of classification algorithms using MCDM and rank correlation, International Journal of Information Technology & Decision Making 11(1) (2012) 197-225.
70. Y. Peng, G. Kou, G. Wang and Y. Shi, FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms, Omega (2011), 39(6) (2011) 677-689.