Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers
Duangsoithong, D and Windeatt, T Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers In: ECML - SUEMA 2010, 2010-09-20 - 2010-09-20, Barcelona, Spain.
Rakkrit Duangsoithong and Terry Windeatt SUEMA10.pdf
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PC and TPDA algorithms are robust and well known prototype algorithms, incorporating constraint-based approaches for causal discovery. However, both algorithms cannot scale up to deal with high dimensional data, that is more than few hundred features. This paper presents hybrid correlation and causal feature selection for ensemble classifiers to deal with this problem. The number of eliminated features, accuracy, the area under the receiver operating characteristic curve (AUC) and false negative rate (FNR) of proposed algorithms are compared with correlation-based feature selection (FCBF and CFS) and causal based feature selection algorithms (PC, TPDA, GS, IAMB).
|Item Type:||Conference or Workshop Item (Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Depositing User :||Symplectic Elements|
|Date Deposited :||17 Apr 2012 08:48|
|Last Modified :||23 Sep 2013 18:55|
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