NITELIK SEÇME PROBLEMI IÇIN DIFERANSIYEL GELIŞIM ALGORITMASI VE YAPAY ARI KOLONISI OPTIMIZASYON TEKNIĞINI KULLANAN MELEZ YÖNTEM

Nitelik seçme işlemi ile özellik uzayı optimum şekilde daraltılarak veri kümesini en iyi şekilde temsil edebilecek niteliklerin bulunması amaçlanır. Bu çalışma sınıflandırma işlemleri üzerinde nitelik seçme problemi için Yapay Arı Kolonisi optimizasyon tekniği ve Diferansiyel Gelişim algoritmasını birleştirerek yeni bir melez yöntem önermektedir. Önerilen algoritma UCI veri kümeleri üzerinde karar ağacı sınıflandırıcısı (J48) kullanılarak test edilmiştir. Deneysel sonuçlar yeni melez yöntemin sınıflandırma işleminin doğruluğunu düşürmeden ya da en az seviyede düşürerek nitelik sayısını azalttığını ve dolayısıyla yeni örneklerin sınıflandırılması için gereken sürenin de azaldığını göstermiştir.

Anahtar Kelimeler:

Nitelik seçme, Hibrit optimizasyon, Yapay arı kolonisi, Diferansiyel gelişim

HYBRID METHOD USING DIFFERENTIAL EVOLUTION ALGORITHM AND ARTIFICIAL BEE COLONY OPTIMIZATION TECHNIQUE FOR FEATURE SELECTION PROBLEM

Aim of the feature selection process is to find the best features which can represent the dataset by narrowing the feature space optimally. This study proposes a new hybrid method which combines Artificial Bee Colony and Differential Evolution algorithms for feature selection problem of classification tasks. The proposed algorithm was tested using decision tree classifier (J48) on UCI datasets. The experimental results show that the new hybrid method reduces the number of features by not decreasing or least decreasing the classification performance and therefore the time which it takes for classification of new instances decreases as well

Keywords:

Feature selection, Hybrid optimization, Artificial bee colony, Differential evolution,

PDF

___

Abdullah A., Deris S., Anwar S. (2011): “Hybrid Evolutionary Clonal Selection for Parameter Estimation of Biological Model”, International Journal of Computer Applications in Engineering Sciences, Cilt 1, No. 3, s.313-319.
Abraham A., Jatoth R.K., Rajasekhar A. (2012): “Hybrid Differential Artificial Bee Colony Algorithm”, Journal of Computational and Theoretical Nanoscience, Cilt 9, No. 2, s.249
Alizadegan A., Meybodi M. R., Asady B. (2012): “A Novel Hybrid Artificial Bee Colony Algorithm and Differential Evolution for Unconstrained Optimization Problems”, Advances in Computer Science and Engineering, Cilt 8, No. 1, s.45-56.
Chen Y., Miao D., Wang R. (2010): “A Rough Set Approach to Feature Selection Based on Ant Colony Optimization”, Pattern Recognition Letters, Elsevier, s.226-233.
Gao W.F., Liu S. (2011): “Improved Artificial Bee Colony Algorithm for Global Optimization”, Information Processing Letters, Elsevier, s.871-882.
Karaboğa D., Baştürk B. (2008): “On the Performance of Artificial Bee Colony (ABC) Algorithm”, Applied Soft Computing, Elsevier, s.687-697.
Keskintürk T. (2006): “Diferansiyel Gelişim Algoritması”, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, Cilt 1, s.85-99.
Khushaba R. N., Al-Ani A., Al-Jumaily A. (2011): “Feature Subset Selection Using Differential Evolution and A Statistical Repair Mechanism”, Expert Systems with Applications, Elsevier, s.11515-11526.
Li X., Yin M. (2012): “Hybrid Differential Evolution with Artificial Bee Colony and Its Application for Design of A Reconfigurable Antenna Array with Discrete Phase Shifters”, IET Microwaves Antennas & Propagation, Cilt 6, No. 14, s.1573–1582.
Mallipeddi R., Suganthan P. N., Pan Q. K., Tasgetiren M. F. (2011): “Differential Evolution Algorithm with Ensemble of Parameters and Mutation Strategies”, Applied Soft Computing, Elsevier, s.1679-1696.
Montgomery J., Chen S. (2010): “An Analysis of the Operation of Differential Evolution at High and Low Crossover Rates”, Evolutionary Computation (CEC), IEEE Congress, s.1-8.
Palanisamy S., Kanmani S. (2012): “Artificial Bee Colony Approach for Optimizing Feature Selection”, International Journal of Computer Science Issues, Cilt 9, No. 3, s.432-438.
Prasartvit T., Banharnsakun A., Kaewkamnerdpong B., Achalakul T. (2013): “Reducing Bioinformatics Data Dimension with ABC-kNN”, Neurocomputing, Elsevier, s.367-381.
Sá Â. A., Andrade A. O., Soares A. B. (2008): “Exploration vs. Exploitation in Differential Evolution”, AISB Convention Communication, Interaction and Social Intelligence, Cilt 1, s.57-63.
Saraç E., Özel S.A. (2010): “URL Tabanlı Web Sayfası Sınıflandırma”, ASYU Sempozyumu, s.13-17.
Schiezaro M.,Pedrini H. (2013): “Data Feature Selection Based on Artificial Bee Colony Algorithm”, EURASIP Journal on Image and Video Processing, Springer US, s.1-8.
Shanthi S., Bhaskaran V.M. (2014): “Modified Artificial Bee Colony Based Feature Selection: A New Method in the Application of Mammogram Image Classification”, International Journal of Science, Engineering and Technology Research, Cilt 3, No. 6, s.1664-1667.
Storn R., Price K. (1997): “Differential Evolution–A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”, Journal of Global Optimization, Springer US, s.341-359.
Xu Y., Fan P., Yuan L. (2013): “A Simple and Efficient Artificial Bee Colony Algorithm”, Mathematical Problems in Engineering, Hindawi, s.1-9.
Yang J., Honavar V. (1998): “Feature Subset Selection Using A Genetic Algorithm”, Feature Extraction, Construction and Selection, Springer US, s.117-136.
Yang Y., Pedersen J. O. (1997): “A Comparative Study on Feature Selection in TextCategorization”, ICML, Cilt 97, s.412-420.
Yusoff S. A. M., Abdullah R., Venkat I. (2014): “Adapted Bio-inspired Artificial Bee Colony and Differential Evolution for Feature Selection in Biomarker Discovery Analysis”, Recent Advances on Soft Computing and Data Mining, Springer International Publishing, s.111
Zhang Y., Wu L., Wang S. (2011): “Magnetic Resonance Brain Image Classification by An Improved Artificial Bee Colony Algorithm”, Progress in Electromagnetics Research, Cilt 116, s.65-79.