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— This paper proposes two extensions to a Multi-Label Keywords-multi-label feature selection, multi-label classification, microarray data I. INTRODUCTION Classification is a data mining task which aims to learn the relationship between the values of the predictor attributes of an instance and its class label(s). This relationship is learned from(More)
We propose three approaches to extend our previous Multi-Label Correlation-based Feature Selection (ML-CFS) method with cancer-related KEGG pathway information, in order to select a better set of genes (features) for cancer microarray data classification. In the approach which produced the best results, ML-CFS was extended with a weighted formula that(More)
This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Multi-Label Correlation-based Feature Selection (LexGA-ML-CFS), which is an extension of the previous single-objective Genetic Algorithm for Multi-label Correlation-based Feature Selection (GA-ML-CFS). This extension uses a LexGA as a global search method for generating candidate(More)
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