Yukihiro Tagami
ABSTRACT
Web scale classification problems, such as Web page tagging and E-commerce product recommendation, are typically regarded as multi-label classification with an extremely large number of labels. In this paper, we propose GPT, which is a novel tree-based approach for extreme multi-label learning. GPT recursively splits a feature space with a hyperplane at each internal node, considering approximate k-nearest neighbor graph on the label space. We learn the linear binary classifiers using a simple optimization procedure. We conducted evaluations on several large-scale real-world data sets and compared our proposed method with recent state-of-the-art methods. Experimental results demonstrate the effectiveness of our proposed method.
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Digital Library
- H. Jain, Y. Prabhu, and M. Varma. Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications. In KDD, 2016. Google ScholarDigital Library
- K. Jasinska, K. Dembczy\'nski, R. Busa-Fekete, K. Pfannschmidt, T. Klerx, and E. Hüllermeier. Extreme f-measure maximization using sparse probability estimates. In ICML, 2016. Google ScholarDigital Library
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- Y. Prabhu and M. Varma. FastXML: A fast, accurate and stable tree-classifier for extreme multi-label learning. In KDD, 2014. Google ScholarDigital Library
Index Terms
- Learning Extreme Multi-label Tree-classifier via Nearest Neighbor Graph Partitioning
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