Feature Selection using Genetic Algorithm to improve SVM Classifier

This book gives a classification algorithms like Support Vector Machine and Genetic Algorithm are used to find the classification accuracy for the Wisconsin Breast Cancer dataset. The benchmark dataset Wisconsin Breast Cancer dataset is obtained from UCI Machine Learning Repository. The dataset consists of 699 instances divided into 2 classes namely Benign and Malignant each with 11 attributes. Support vector machines (SVMs) are a set of related supervised learning methods used for classification. A classification SVM model attempts to separate the target classes with the widest possible margin. In SVM Radial basis function and Polynomial kernel function are used to calculate classification accuracy and run time. Feature Selection is used to improve the accuracy of the SVM classifier.In GA Integer and Binary Coded Genetic Algorithm are also used to calculate classification accuracy and run time. Integer- Coded Genetic Algorithm is used to select important and relevant features for classification. Binary Coded Genetic Algorithm can be applied to many optimization problems which contains binary string for the variables.