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

- Huda, Shamsul, Yearwood, John, Stranieri, Andrew

Title
Hybrid wrapper-filter approaches for input feature selection using maximum relevance and Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA)
Creator
Huda, Shamsul; Yearwood, John; Stranieri, Andrew
Date
2010
Type
Text; Conference proceedings
Identifier
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/41311
Identifier
vital:3831
Abstract
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.
Publisher
Melbourne, VIC
Rights
Open Access
Rights
This metadata is freely available under a CCO license
Subject
Artificial Neural Network; Bench marks; Data mining applications; Data sets; Feature ranking; Feature selection; Feature selection algorithm; Feature sets; Feature subset; Filter approach; Filter method; Input features; Machine-learning; Mutual informations; Research problems; Search process; Speed-ups; Wrapper approach; Wrapper-based approach; Feature extraction; Filtration; Gain measurement; Heuristic methods; Network security; Neural networks
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