A Novel Methodology for Feature Subset Selection using TLBO Algorithm

In the present paper, a novel method for Feature Subset Selection in dataset, FSS-TLBOA (Feature Subset Selection by Teaching Learning Based Optimization Algorithm), is proposed. A dataset can contain several features. Many Clustering methods are designed for clustering low–dimensional data. In high dimensional space finding clusters of data objects is challenging due to the curse of dimensionality. When the dimensionality increases, data in the irrelevant dimensions may produce much noise and mask the real clusters to be discovered. To deal with these problems, an efficient feature subset selection technique for high dimensional data has been proposed. Feature subset selection reduces the data size by removing irrelevant or redundant attributes. Experiments are performed on the bank dataset to classify, according to the 11 existing features, with the help of TLBO (teacher learning based optimization) algorithm. This paper describes the main idea of Feature Subset Selection, presenting related work about each concept. Its aim is to improve the performance results of classifiers but using a significantly reduced set of features.