Associative classification framework for cancer microarray data - Fang, Ong Huey; Mustapha, Norwati; Mustapha, Aida; Hamdan, Hazlina; Rosli, Rozita (American Scientific Publishers, 2017)
Having good cancer classifiers are crucial in order to give the most effective and cost saving treatments for patients. Microarray is one of the vital tools in cancer studies, as it allows the discovery of gene expression patterns and promises better accuracy of cancer classification. This paper presents an associative classification framework for microarray data. The proposed framework combined the strength of both filter method and association rule mining. The experimental results showed that the selected gene subsets from generated association rules can improve the accuracy and interpretability of classifiers.