Full model selection using Bat algorithm

Author

Bansal, Bhavna ; Sahoo, Anita

Author_Institution

Comput. Sci. Dept., JSS Acad. of Tech. Educ., Noida, India

fYear

2015

fDate

3-4 March 2015

Firstpage

1

Lastpage

4

Abstract

Full Model Selection (FMS) selects the optimal amalgamation of pre-processing technique, feature subset and learning algorithm that obtains the least classification error for a given dataset. Meta-heuristic optimization algorithms are quite suitable for FMS, since it needs to explore and exploit a large solution space. This paper investigates the ability of an efficient meta-heuristic, named Bat algorithm for FMS. Traditional Bat algorithm has been modified and applied for FMS in gene expression analysis. Experiments are conducted on Gene Expression benchmark datasets that shows the suitability and effectiveness of the proposed approach in FMS.

Keywords

evolutionary computation; feature selection; learning (artificial intelligence); optimisation; FMS; bat algorithm; feature subset; full model selection; gene expression analysis; gene expression benchmark datasets; learning algorithm; least classification error; meta-heuristic optimization algorithms; optimal amalgamation selection; preprocessing technique; Algorithm design and analysis; Classification algorithms; Computational modeling; Gene expression; Mathematical model; Sociology; Statistics; Bat Algorithm; Classification; Feature selection; Machine Learning; Meta-heuristics; Model selection;

fLanguage

English

Publisher

ieee

Conference_Titel

Cognitive Computing and Information Processing (CCIP), 2015 International Conference on

Conference_Location

Noida

Type

conf

DOI

10.1109/CCIP.2015.7100693

Filename

7100693