A new classification method for breast cancer diagnosis: feature selection artificial immune recognition system (FS-AIRS)

Authors:
Kemal Polat;Seral Sahan;Halife Kodaz;Salih Günes
Affiliations:
Eng.-Arch. Fac. Electrical & Electronics Eng., Selcuk University, Konya, Turkey;Eng.-Arch. Fac. Electrical & Electronics Eng., Selcuk University, Konya, Turkey;Eng.-Arch. Fac. Computer Eng., Selcuk University, Konya, Turkey;Eng.-Arch. Fac. Electrical & Electronics Eng., Selcuk University, Konya, Turkey
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with a new approach, FS-AIRS (Feature Selection Artificial Immune Recognition System) algorithm that has an important place in classification systems and was developed depending on the Artificial Immune Systems. With this purpose, 683 data in the Wisconsin breast cancer dataset (WBCD) was used. In this study, differently from the studies in the literature related to this concept, firstly, the feature number of each data was reduced to 6 from 9 in the feature selection sub-program by means of forming rules related to the breast cancer data with the C4.5 decision tree algorithm. After separating the 683 data set with reduced feature number into training and test sets by 10 fold cross validation method in the second stage, the data set was classified in the third stage with AIRS and a quite satisfying result was obtained with respect to the classification accuracy compared to the other methods used for this classification problem.