An Effective Multilabel Classification Using Feature Selection http://localhost:8080/xmlui/handle/123456789/2152 2026-06-23T06:21:10Z 2026-06-23T06:21:10Z S. S. Sane, Prajakta Chaudhari V. S. Tidake Abstract http://localhost:8080/xmlui/handle/123456789/2153 2019-07-15T06:01:16Z 2017-04-15T00:00:00Z

Title: An Effective Multilabel Classification Using Feature Selection Authors: S. S. Sane, Prajakta Chaudhari; V. S. Tidake Abstract Abstract: 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.

2017-04-15T00:00:00Z