Indrajit Mandal and Sairam.N, Sastra University, India

Optimization problems are dominantly being solved using Computational Intelligence. One of the issues that can be addressed in this context is problems related to attribute subset selection evaluation. This paper presents a computational intelligence technique for solving the optimization problem using a proposed model called Modified Genetic Search Algorithms (MGSA) that avoids local bad search space with merit and scaled fitness variables, detecting and deleting bad candidate chromosomes, thereby reducing the number of individual chromosomes from search space and subsequent iterations in next generations. This paper aims to show that Rotation forest ensembles are useful in the feature selection method. The base classifier is multinomial logistic regression method integrated with Haar wavelets as projection filter and reproducing the ranks of each features with 10 fold cross validation method. It also discusses the main findings and concludes with promising result of the proposed model. It explores the combination of MGSA for optimization with Naïve Bayes classification. The result obtained using proposed model MGSA is validated mathematically using Principal Component Analysis. The goal is to improve the accuracy and quality of diagnosis of Breast cancer disease with robust machine learning algorithms.As compared to other works in literature survey, experimental results achieved in this paper show better results with statistical inference.

Computational Intelligence, attribute subset selection, Rotation forest, Haar wavelets, Modified Genetic Search algorithm.