A hybrid classification approach Based on Improved Differential ‎Evolution Algorithm for Breast Cancer Diagnosis

Breast cancer is one of the most common malignant tumors and the main cause of cancer death ‎among women worldwide. The diagnosis of this type of cancer is a challenging problem in cancer ‎diagnosis researches. Several research before have proved that ensemble based machine learning ‎classifiers are able to detect breast cancer spot more accurate. However, the success of an ensemble ‎classifier highly depends on the choice of method to combine the outputs of the classifiers into a ‎single one. This paper proposes a novel ensemble method that uses modified differential evolution ‎‎(DE) algorithm generated weights to create ensemble of classifiers for improving the accuracy of ‎breast cancer diagnosis. This paper proposes an ensemble-based classifier to improve the accuracy ‎of breast cancer diagnosis. As the performance of DE algorithm is strongly influenced by selection ‎of its control parameters, local unimodal sampling (LUS) technique is used to find these parameters. ‎The two most popular classifiers support vector machine (SVM) and K-nearest neighbor (KNN) ‎classifiers are used in the ensemble. The classification is then carried out using the majority vote of ‎the ensemble. The accuracy of the presented model is compared to other approaches from literature ‎using standard dataset. The experimental results based on breast cancer dataset show that the ‎proposed model outperforms other classifiers in breast cancer abnormalities classification with ‎‎۹۹.۴۶% accuracy.‎

Keywords:

Breast cancer diagnosis , local unimodal sampling , differential evolution algorithm , majority vote , ‎Accuracy.‎