Learning Extreme Multi-label Tree-classifier via Nearest Neighbor Graph Partitioning
WWW 2017 (The 26th International Conference on World Wide Web) Posters, 2017/4
機械学習 (Machine Learning) データサイエンス (Data Science)
- 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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