Hybrid correlation and causal feature selection for ensemble classifiers
Duangsoithong, R and Windeatt, T (2011) Hybrid correlation and causal feature selection for ensemble classifiers Studies in Computational Intelligence, 373. pp. 97-115.
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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 chapter presents hybrid correlation and causal feature selection for ensemble classifiers to deal with this problem. Redundant features are removed by correlation-based feature selection and then irrelevant features are eliminated by causal feature selection. 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).
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Identification Number :||https://doi.org/10.1007/978-3-642-22910-7_6|
|Additional Information :||The original publication is available at http://www.springerlink.com|
|Depositing User :||Symplectic Elements|
|Date Deposited :||17 Feb 2012 09:19|
|Last Modified :||23 Sep 2013 18:55|
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