Semi-supervised learning algorithm for binary relevance multi-label classification

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The presented paper describes our model for the WISE 2014 challenge multi-label classification task. The goal of the challenge was to implement a multi-label text classification model which maximizes the mean F1 score on a private test data. The described method involves a binary relevance scheme with linear classifiers trained using stochastic gradient descent. A novel method for determining the values of classifiers’ meta-parameters was developed. In addition, our solution employs the semi-supervised learning which significantly improves the evaluation score. The presented solution won the third place in the challenge. The results are discussed and the supervised and semi-supervised approaches are compared.




Švec, J. (2015). Semi-supervised learning algorithm for binary relevance multi-label classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9051, pp. 1–13). Springer Verlag.

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