Memetic feature selection algorithm for multi-label classification

作者：Lee Jaesung; Kim Dae Won^*

来源：Information Sciences, 2015, 293: 80-96.

DOI：10.1016/j.ins.2014.09.020

• Multi-label feature selection
• Memetic algorithm
• Local refinement

The use of multi-label classification, i.e., assigning unseen patterns to multiple categories, has emerged in modern applications. A genetic-algorithm based multi-label feature selection method has been considered useful because it successfully improves the accuracy of multi-label classification. However, genetic algorithms are limited to identify fine-tuned feature subsets that are close to the global optimum, which results in a long runtime. In this paper, we present a memetic feature selection algorithm for multi-label classification that prevents premature convergence and improves the efficiency. The proposed method employs memetic procedures to refine the feature subsets found through a genetic search, resulting in an improvement in multi-label classification. Empirical studies using various tests show that the proposed method outperforms conventional multi-label feature selection methods.

• 出版日期2015-2-1