Suwimol Jungjit

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This paper proposes two extensions to a Multi-Label Correlation Based Feature Selection Method (ML-CFS): (1) MLCFS using the absolute value of the correlation coefficient in the equation for evaluating a candidate feature subset, and (2) MLCFS using Mutual Information for class label weighting. These extensions are evaluated in a bioinformatics case study(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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