¹College of Computer Science and Technology, Jilin University, Changchun, P. R. China
²College of Computer, Northeast Normal University, Changchun, P. R. China

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Abstract—Many real-world classification applications fall into the class of positive and unlabeled learning problems. The existing techniques almost all are based on the two-step strategy. This paper proposes a new reliable negative extracting algorithm for step 1. We adopt kNN algorithm to rank the similarity of unlabeled examples from the k nearest positive examples, and set a threshold to label some unlabeled examples that lower than it as the reliable negative examples rather than the common method to label positive examples. In step 2, we use iterative SVM technique to refine the finally classifier. Our proposed method is simplicity and efficiency and on some level independent to k. Experiments on the popular Reuter21578 collection show the effectiveness of our proposed technique.

Index Terms—Learning from Positive and Unlabeled examples, k Nearest Neighbor, Text Classification, Support Vector Machine, Information Retrieval

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Cite: Bangzuo Zhang, Wanli Zuo, "Reliable Negative Extracting Based on kNN for Learning from Positive and Unlabeled Examples," Journal of Computers vol. 4, no. 1, pp. 94-101, 2009.