Interactive classification-based systems engage users to coach learning algorithms to take into account their own individual preferences. However most of the recent interactive systems limit the users to a single-label classification, which may be not expressive enough in some organization tasks such as film classification, where a multi-label scheme is required. The objective of this paper is to compare the behaviors of 12 multi-label classification methods in an interactive framework where “good” predictions must be produced in a very short time from a very small set of multi-label training examples. Experimentations highlight important performance differences for four complementary evaluation measures (Log-Loss, Ranking-Loss, Learning and Prediction Times). The best results are obtained for Multi-label k Nearest Neighbors (ML-kNN), ensemble of classifier chains (ECC), and ensemble of binary relevance (EBR).
CITATION STYLE
Nair-Benrekia, N. Y., Kuntz, P., & Meyer, F. (2015). Selecting a multi-label classification method for an interactive system. In Studies in Classification, Data Analysis, and Knowledge Organization (Vol. 48, pp. 157–167). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-662-44983-7_14
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