A Study on Semantic Similarity of Gene Ontology Using Clustering

Abstract: Feature selection has been an active research area in pattern recognition, statistics and data mining community. Idea behind feature selection is to choose a subset of input variables by eliminating features with little or no predictive information. Feature selection (FS) is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. This can significantly improve the comprehensibility of the resulting classifier models and often build a model that generalizes better to unseen points. Rough set theory (RST) can be used as a tool to discover data dependencies and to reduce the number of attributes contained in a dataset using the data alone, requiring no additional information. In this paper, feature selection technique has been used in high dimensional data for removing irrelevant features and producing high accuracy for post processing data.

Keywords: Feature Selection, Clustering, Rough set theory, Quick Reduct, Fast Correlation Based Filter.