Abstract
Semi-supervised learning has been the focus of machine learning and data mining research in the past few years. Various algorithms and techniques have been proposed, from generative models to graph-based algorithms. In this work, we focus on the Cluster-and-Label approaches for semi-supervised classification. Existing cluster-and-label algorithms are based on some underlying models and/or assumptions. When the data fits the model well, the classification accuracy will be high. Otherwise, the accuracy will be low. In this paper, we propose a refinement approach to address the model misfit problem in semi-supervised classification. We show that we do not need to change the cluster-and-label technique itself to make it more flexible. Instead, we propose to use successive refinement clustering of the dataset to correct the model misfit. A series of experiments on UCI benchmarking data sets have shown that the proposed approach outperforms existing cluster-and-label algorithms, as well as traditional semi-supervised classification techniques including Selftraining and Tri-training.
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Su, H., Chen, L., Ye, Y., Sun, Z., Wu, Q. (2010). A Refinement Approach to Handling Model Misfit in Semi-supervised Learning. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_8
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DOI: https://doi.org/10.1007/978-3-642-17313-4_8
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