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Hybrid wrapper-filter approaches for input feature selection using maximum relevance and Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA)

conference contribution
posted on 2010-12-27, 00:00 authored by Shamsul HudaShamsul Huda, John YearwoodJohn Yearwood, A Strainieri
Feature selection is an important research problem in machine learning and data mining applications. This paper proposes a hybrid wrapper and filter feature selection algorithm by introducing the filter's feature ranking score in the wrapper stage to speed up the search process for wrapper and thereby finding a more compact feature subset. The approach hybridizes a Mutual Information (MI) based Maximum Relevance (MR) filter ranking heuristic with an Artificial Neural Network (ANN) based wrapper approach where Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA) has been combined with MR (MR-ANNIGMA) to guide the search process in the wrapper. The novelty of our approach is that we use hybrid of wrapper and filter methods that combines filter's ranking score with the wrapper-heuristic's score to take advantages of both filter and wrapper heuristics. Performance of the proposed MRANNIGMA has been verified using bench mark data sets and compared to both independent filter and wrapper based approaches. Experimental results show that MR-ANNIGMA achieves more compact feature sets and higher accuracies than both filter and wrapper approaches alone. © 2010 IEEE.

History

Pagination

442 - 449

Publisher

IEEE

Location

Melbourne, Vic.

Place of publication

Piscataway, N.J.

Start date

2010-09-01

End date

2010-09-03

ISBN-13

9780769541594

Publication classification

EN.1 Other conference paper

Title of proceedings

Proceedings - 2010 4th International Conference on Network and System Security, NSS 2010

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