Multi-label classification of documents using fine-grained weights and modified co-training

Article type: Research Article

Authors: Lee, Chang-Hwan

Affiliations: Department of Information & Communications, Dongguk University, Seoul 100-715, Korea | Tel.: +82 2 2260 3801; E-mail: [email protected]

Abstract: This paper use multinomial nave Bayes to improve multi-label classification methods in a number of ways. First, we use the value weighting method, a new fine-grained weighting method, to calculate the weights of the feature values. Second, we employ a co-training method to incorporate the dependencies among the class values. The results of our experiments show that the proposed approach outperforms other state-of-the-art methods.

Keywords: Multi-label classification, multinomial naive Bayes, fine-grained weights, co-training

DOI: 10.3233/IDA-163264

Journal: Intelligent Data Analysis, vol. 22, no. 1, pp. 103-115, 2018