e-ISSN 2231-8542
ISSN 1511-3701

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Feature Selection Methods: Case of Filter and Wrapper Approaches for Maximising Classification Accuracy

Yap Bee Wah, Nurain Ibrahim, Hamzah Abdul Hamid, Shuzlina Abdul-Rahman and Simon Fong

Pertanika Journal of Tropical Agricultural Science, Volume 26, Issue 1, January 2018

Keywords: Feature selection methods, filter method, logistic regression, simulation, wrapper method

Published on: 18 Jan 2018

Feature selection has been widely applied in many areas such as classification of spam emails, cancer cells, fraudulent claims, credit risk, text categorisation and DNA microarray analysis. Classification involves building predictive models to predict the target variable based on several input variables (features). This study compares filter and wrapper feature selection methods to maximise the classifier accuracy. The logistic regression was used as a classifier while the performance of the feature selection methods was based on the classification accuracy, Akaike information criteria (AIC), Bayesian information criteria (BIC), Area Under Receiver operator curve (AUC), as well as sensitivity and specificity of the classifier. The simulation study involves generating data for continuous features and one binary dependent variable for different sample sizes. The filter methods used are correlation based feature selection and information gain, while the wrapper methods are sequential forward and sequential backward elimination. The simulation was carried out using R, an open-source programming language. Simulation results showed that the wrapper method (sequential forward selection and sequential backward elimination) methods were better than the filter method in selecting the correct features.

ISSN 1511-3701

e-ISSN 2231-8542

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