An incremental feature reordering (IFR) algorithm to classify eye state identification using EEG

Abstract

In this work investigation of eye state identification is performed using EEG system. Binary classifications developed to categories the eye state in the classes open and closed. Feature subset selection is one of the important steps in classification. The present work is for finding feature subset selection named as Incremental Feature Reordering (IFR), it gives most non dominant feature (MND) for Electroencephalography (EEG) signal corpus and create reorder set. The removal of MND gives optimal subset feature and it increases the classifier accuracy and efficiency. The data structure used here is a two way doubly linked list, it creates dynamic environment for reordering the ordered set.