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- W1978557614 abstract "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 feature subsets. In our experiments, we compare the results obtained by LexGA-ML-CFS with the results obtained by the original hill climbing-based ML-CFS, the single-objective GA-ML-CFS and a baseline Binary Relevance method, using ML-kNN as the multi-label classifier. The results from our experiments show that LexGA-ML-CFS improved predictive accuracy, by comparison with other methods, in some cases, but in general there was no statistically significant different between the results of LexGA-ML-CFS and other methods." @default.
- W1978557614 created "2016-06-24" @default.
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- W1978557614 date "2015-07-11" @default.
- W1978557614 modified "2023-09-23" @default.
- W1978557614 title "A Lexicographic Multi-Objective Genetic Algorithm for Multi-Label Correlation Based Feature Selection" @default.
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- W1978557614 doi "https://doi.org/10.1145/2739482.2768448" @default.
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