Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification

The Scientific World Journal

Volumn 2013, Issue , 2013, Pages

  Uzer, Mustafa Serter ^a   Yilmaz, Nihat ^a   Inan, Onur ^a  

^a SELCUK UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ANTIGEN RECOGNITION; ARTICLE; ARTIFICIAL BEE COLONY ALGORITHM; CLASSIFICATION ALGORITHM; CLASSIFIER; CLINICAL CLASSIFICATION; COMPUTER ASSISTED DIAGNOSIS; DATA BASE; DECISION TREE; DIABETES MELLITUS; DISCRIMINANT ANALYSIS; FORAGING BEHAVIOR; HEPATITIS; HUMAN; LEARNING ALGORITHM; LIVER DISEASE; PATTERN RECOGNITION; PREDICTIVE VALUE; PRINCIPAL COMPONENT ANALYSIS; QUALITY OF LIFE; RESOURCE ALLOCATION; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; ADULT; AGED; ALGORITHM; ANIMAL; BEE; CLASSIFICATION; DISEASES; FACTUAL DATABASE; FEMALE; MIDDLE AGED; PREGNANCY; VERY ELDERLY; YOUNG ADULT;

ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; ANIMALS; BEES; DATABASES, FACTUAL; DISEASE; FEMALE; HUMANS; MIDDLE AGED; PREGNANCY; SUPPORT VECTOR MACHINES; YOUNG ADULT;

EID: 84883204011     PISSN: None     EISSN: 1537744X     Source Type: Journal    
DOI: 10.1155/2013/419187     Document Type: Article

References (35)

1. Polat K., Güneş S., Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation. Digital Signal Processing 2006 16 6 889 901 2-s2.0-33751175417 10.1016/j.dsp.2006.07.005
2. Polat K., Güneş S., Medical decision support system based on artificial immune recognition immune system (AIRS), fuzzy weighted pre-processing and feature selection. Expert Systems with Applications 2007 33 2 484 490 2-s2.0-33846442797 10.1016/j.eswa.2006.05.013
3. Polat K., Güneş S., Prediction of hepatitis disease based on principal component analysis and artificial immune recognition system. Applied Mathematics and Computation 2007 189 2 1282 1291 2-s2.0-34249031893 10.1016/j.amc.2006.12.010
4. Kahramanli H., Allahverdi N., Extracting rules for classification problems: AIS based approach. Expert Systems with Applications 2009 36 7 10494 10502 2-s2.0-67349236666 10.1016/j.eswa.2009.01.029
5. Dogantekin E., Dogantekin A., Avci D., Automatic hepatitis diagnosis system based on linear discriminant analysis and adaptive network based on fuzzy inference system. Expert Systems with Applications 2009 36 8 11282 11286 2-s2.0-67349267265 10.1016/j.eswa.2009.03.021
6. Bascil M. S., Temurtas F., A study on hepatitis disease diagnosis using multilayer neural network with Levenberg Marquardt training algorithm. Journal of Medical Systems 2011 35 3 433 436 2-s2.0-79959751877 10.1007/s10916-009-9378- 2
7. Lee Y. J., Mangasarian O. L., SSVM: a smooth support vector machine for classification. Computational Optimization and Applications 2001 20 1 5 22 2-s2.0-0035479871 10.1023/A:1011215321374
8. van Gestel T., Suykens J. A. K., Lanckriet G., Lambrechts A., de Moor B., Vandewalle J., Bayesian framework for least-squares support vector machine classifiers, Gaussian processes, and Kernel Fisher discriminant analysis. Neural Computation 2002 14 5 1115 1147 2-s2.0-0036582564 10.1162/089976602753633411
9. Gonçalves L. B., Vellasco M. M. B. R., Pacheco M. A. C., de Souza F. J., Inverted Hierarchical Neuro-Fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases. IEEE Transactions on Systems, Man and Cybernetics C 2006 36 2 236 248 2-s2.0-33645970704 10.1109/TSMCC.2004.843220
10. Özşen S., Güneş S., Effect of feature-type in selecting distance measure for an artificial immune system as a pattern recognizer. Digital Signal Processing 2008 18 4 635 645 2-s2.0-44449104946 10.1016/j.dsp.2007.08.004
11. Li D. C., Liu C. W., Hu S. C., A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets. Artificial Intelligence in Medicine 2011 52 1 45 52 2-s2.0-79956012303 10.1016/j.artmed.2011.02.001
12. Chen L. F., Su C. T., Chen K. H., Wang P. C., Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis. Neural Computing and Applications 2012 21 8 2087 2096 2-s2.0-79955878348 10.1007/s00521-011-0632-4
13. Chang P. C., Lin J. J., Liu C. H., An attribute weight assignment and particle swarm optimization algorithm for medical database classifications. Computer Methods and Programs in Biomedicine 2012 107 3 382 392 2-s2.0-78650534156 10.1016/j.cmpb.2010.12.004
14. Şahan S., Polat K., Kodaz H., Güneş S., The medical applications of attribute weighted artificial immune system (AWAIS): diagnosis of heart and diabetes diseases. Artificial Immune Systems 2005 3627 456 468 Lecture Notes in Computer Science 2-s2.0-26944458433
15. Polat K., Güneş S., An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digital Signal Processing 2007 17 4 702 710 2-s2.0-34249285900 10.1016/j.dsp.2006.09.005
16. Polat K., Güneş S., Arslan A., A cascade learning system for classification of diabetes disease: generalized discriminant analysis and least square support vector machine. Expert Systems with Applications 2008 34 1 482 487 2-s2.0-34248592215 10.1016/j.eswa.2006.09.012
17. Kahramanli H., Allahverdi N., Design of a hybrid system for the diabetes and heart diseases. Expert Systems with Applications 2008 35 1-2 82 89 2-s2.0-44949105491 10.1016/j.eswa.2007.06.004
18. Patil B. M., Joshi R. C., Toshniwal D., Hybrid prediction model for t ype-2 diabetic patients. Expert Systems with Applications 2010 37 12 8102 8108 2-s2.0-77957831716 10.1016/j.eswa.2010.05.078
19. Isa N. A. M., Mamat W. M. F. W., Clustered-hybrid multilayer perceptron network for pattern recognition application. Applied Soft Computing Journal 2011 11 1 1457 1466 2-s2.0-77957899139 10.1016/j.asoc.2010.04.017
20. Aibinu A. M., Salami M. J. E., Shafie A. A., A novel signal diagnosis technique using pseudo complex-valued autoregressive technique. Expert Systems with Applications 2011 38 8 9063 9069 2-s2.0-79953715934 10.1016/j.eswa.2010.11. 005
21. Pudil P., Novovičová J., Kittler J., Floating search methods in feature selection. Pattern Recognition Letters 1994 15 11 1119 1125 2-s2.0-0028547556
22. Ladha L., Deepa T., Feature selection methods and algorithms. International Journal on Computer Science and Engineering 2011 3 5 1787 1797
23. Sasikala M., Kumaravel N., Comparison of feature selection techniques for detection of malignant tumor in brain images. Proceedings of the Annual IEEE INDICON '05 December 2005 212 215 2-s2.0-33847169952 10.1109/INDCON.2005.1590157
24. Karaboga D., Ozturk C., A novel clustering approach: artificial bee colony (ABC) algorithm. Applied Soft Computing Journal 2011 11 1 652 657 2-s2.0-77957901422 10.1016/j.asoc.2009.12.025
25. Karaboga D., Akay B., A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation 2009 214 1 108 132 2-s2.0-67349273050 10.1016/j.amc.2009.03.090
26. Akay B., Karaboga D., A modified artificial bee colony algorithm for real-parameter optimization. Information Sciences 2012 192 120 142 2-s2.0-84857832525 10.1016/j.ins.2010.07.015
27. Cortes C., Vapnik V., Support-vector networks. Machine Learning 1995 20 3 273 297 2-s2.0-34249753618 10.1007/BF00994018
28. Amini H., Gholami R., Monjezi M., Torabi S. R., Zadhesh J., Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Computing and Applications 2012 21 8 2077 2085 2-s2.0-79956272988 10.1007/s00521-011-0631-5
29. Chen X. B., Yang J., Liang J., A flexible support vector machine for regression. Neural Computing and Applications 2012 21 8 2005 2013 2-s2.0-79956199033 10.1007/s00521-011-0623-5
30. Ivanciuc O., Reviews in Computational Chemistry 2007 edited by K. B. Lipkowitz and T. R. Cundari
31. Kuan T. W., Wang J. F., Wang J. C., Lin P. C., Gu G. H., VLSI design of an SVM learning core on sequential minimal optimization algorithm. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2012 20 4 673 683 2-s2.0-84859009542 10.1109/TVLSI.2011.2107533
32. Polat K., Güneş S., Breast cancer diagnosis using least square support vector machine. Digital Signal Processing 2007 17 4 694 701 2-s2.0-34249317613 10.1016/j.dsp.2006.10.008
33. François D., Rossi F., Wertz V., Verleysen M., Resampling methods for parameter-free and robust feature selection with mutual information. Neurocomputing 2007 70 7-9 1276 1288 2-s2.0-33847674996 10.1016/j.neucom.2006. 11.019
34. Diamantidis N. A., Karlis D., Giakoumakis E. A., Unsupervised stratification of cross-validation for accuracy estimation. Artificial Intelligence 2000 116 1-2 1 16 2-s2.0-0033903664 10.1016/S0004-3702(99)00094-6
35. Blake C. L., Merz C. J., University of California at Irvine Repository of Machine Learning Databases 1998. http://www.ics.uci.edu/mlearn/MLRepository. html