Application of genetic algorithms and Gaussian Naïve Bayesian approach in pipeline for cognitive state classification
Shantipriya Parida, Satchidananda Dehuri, Sung‐Bae Cho · 2014
In this paper an application of genetic algorithms (GAs) and Gaussian Naïve Bayesian (GNB) approach is studied to explore the brain activities by decoding specific cognitive states from functional magnetic resonance imaging (fMRI) data. However, in case of fMRI data analysis the large number of attributes may leads to a serious problem of classifying cognitive states. It significantly increases the computational cost and memory usage of a classifier. Hence to address this problem, we use GAs for selecting optimal set of attributes and then GNB classifier in a pipeline to classify different cognitive states. The experimental outcomes prove its worthiness in successfully classifying different cognitive states. The detailed comparison study with popular machine learning classifiers illustrates the importance of such GA-Bayesian approach applied in pipeline for fMRI data analysis.