Experimental Exploration of Support Vector Machine for Cancer Cell Classification

text classification is the task of automatically categorizing collections of electronic textual documents into their predefined classes, based on their contents. Due to the increase in the amount of text data in these recent years, document classification has emerged in the form of text classification systems. They have been widely implemented in a large number of applications such as spam filtering, e-mails, knowledge repositories and ontology mapping. The main essence is to propose a text classification technique based on the feature selection and reduction of the feature vector dimensionality and increase the classification accuracy using pre-processing. This paper gives the detailed study on how support vector machine (SVM) can be used to classify uncertain data. SVM is a powerful and supervised learning sample based on the lowest structural risk principle. During training, this algorithm creates a hyperplane for separating positive and negative samples. The type of kernel used for SVM classifier will be having a major impact on classification results. In this paper Breast Cancer Wisconsin (Diagnostic) Data Sets are used in order to classify using four types of SVM kernel methods such as linear, polynomial, sigmoid and radial. Classification results obtained reveal that radial kernel method is best-suited data sets. In order to measure the suitability of kernel method, various factors are compared from classification results such as accuracy, kappa value, sensitivity, specificity precision etc.