CARSVM: A class association rule-based classification framework and its application to gene expression data

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摘要

ObjectiveIn this study, we aim at building a classification framework, namely the CARSVM model, which integrates association rule mining and support vector machine (SVM). The goal is to benefit from advantages of both, the discriminative knowledge represented by class association rules and the classification power of the SVM algorithm, to construct an efficient and accurate classifier model that improves the interpretability problem of SVM as a traditional machine learning technique and overcomes the efficiency issues of associative classification algorithms.

论文关键词:Machine learning,Association rule mining,Associative classifiers,Support vector machine,Data mining,Gene expression analysis,Gene expression classification,Gene selection

论文评审过程:Received 12 December 2007, Revised 10 May 2008, Accepted 13 May 2008, Available online 30 June 2008.

论文官网地址:https://doi.org/10.1016/j.artmed.2008.05.002