A Filter Correlation Method for Feature Selection

Author

Hosni, Hanen ; Mhamdi, Faouzi

Author_Institution

Nat. Super. Sch. of Eng. of Tunis, Univ. of Tunis, Tunis, Tunisia

fYear

2014

fDate

1-5 Sept. 2014

Firstpage

59

Lastpage

63

Abstract

Biological data is undergoing exponential growth in volume and complexity. Often, the selection of biological features is a crucial step that aims to defy the curse of dimensionality to improve prediction performance in classification systems, facilitate viewing, understanding and analyzing data. In this paper we present an adaptation of the Fast Correlation Based Filter algorithm (FCBF) whose aims is to identify relevant, not redundant features to improve the capacity of prediction and reduce the search space.

Keywords

bioinformatics; data mining; feature selection; information filtering; FCBF; biological data; biological feature selection; classification system prediction performance improvement; curse of dimensionality; data analysis; data understanding; data viewing; fast correlation based filter algorithm; filter correlation method; prediction capacity improvement; search space reduction; Algorithm design and analysis; Biology; Classification algorithms; Correlation; Erbium; Filtering algorithms; Support vector machines; KDD; bioinformatics; biological macromolecules; correlation; feature selection; filter approach;

fLanguage

English

Publisher

ieee

Conference_Titel

Database and Expert Systems Applications (DEXA), 2014 25th International Workshop on

Conference_Location

Munich

ISSN

1529-4188

Print_ISBN

978-1-4799-5721-7

Type

conf

DOI

10.1109/DEXA.2014.28

Filename

6974827