Recently, multilabel classification has received significant attention during the past years. A multilabel classification approach called coupled k-nearest neighbors algorithm for multilabel classification (called here as CK-STC) reported in the literature exploits coupled label similarities between the labels and provides improved performance [Liu and Cao in A Coupled k-Nearest Neighbor Algorithm for Multi-label Classification, pp. 176–187, 2015]. A multilabel feature selection is presented in Li et al. [Multi-label Feature Selection via Information Gain, pp. 346–355, 2014] and called as FSVIG here. FSVIG uses information gain that shows better performance when used with ML-NB, ML-kNN, and RandSvm when compared with existing multilabel feature selection algorithms.This paper investigates the performance of FSVIG when used with CK-STC and compares its performance with other multilabel feature selection algorithms available in MULAN using standard multilabel datasets. Experimental results show that FSVIG when used with CK-STC provides better performance in terms of average precision and one-error.
CITATION STYLE
Sane, S. S., Chaudhari, P., & Tidake, V. S. (2018). An effective multilabel classification using feature selection. In Advances in Intelligent Systems and Computing (Vol. 673, pp. 129–142). Springer Verlag. https://doi.org/10.1007/978-981-10-7245-1_14
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