An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins

Abstract

Communication triggered for cell survival/apoptosis is achieved by three different input proteins. In this paper, we have considered the heat map image that shows 11 different proteins for the HT carcinoma cells which helps in cell survival/apoptosis. Based on the introduction and integration of an algorithm in the classification model, feature selection was divided into three main categories namely: filtering method (FM), wrapper method (WM), and Embedded Method (EM). After applying the feature selection (FS) algorithm, we obtained 7 different marker proteins but out of these proteins, this paper concentrates on only one of them, the AkT which is used for classification using k-nearest neighbour (kNN) classifier and support vector machine (SVM) classifier for calculating predicted mean, standard deviation ratio, and correlation. For kNN, we have used different distance approaches (Euclidean, city block), while for SVM, linear, polynomial, RBF and sigmoid kernels are used for Tier 1 and Tier 2. Results with linear Tier 1 using SVM and Euclidean distance outperform other methods. An accuracy of 76.9% and 84.6% was obtained using the kNN and SVM classifiers respectively with GLDS features. The results obtained gave a better performance when compared with the result of other research papers.

Keywords

• feature selection
• wrapper method
• filter method
• classification
• akt