Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining

  title={Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining},
  author={Kang Peng and Slobodan Vucetic and Bo Han and Hongbo Xie and Zoran Obradovic},
Predictive data mining typically relies on labeled data without exploiting a much larger amount of availabl e unlabeled data. The goal of this paper is to show t hat using unlabeled data can be beneficial in a range o f important prediction problems and therefore should be an integral part of the learning process. Given an unl abeled dataset representative of the underlying distributi on and a K-class labeled sample that might be biased, our approach is to learn K contrast classifiers each tr… CONTINUE READING


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