Evaluating PU Learning Based on Associative Classification Algorithm
Yang Jianlin(), Liu Yang
School of Information Management, Nanjing University, Nanjing 210023, China Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
[Objective] We examine the PU learning with the associative classification algorithm CBA. [Methods] First, we categorized α% of positive examples as unidentified positive examples, which were used to construct the corpus along with negative samples. Then, we classified examples based on all positive class association rules. Finally, we evaluated the reliability of class association rules with relative confidence. [Results] We used 0%, 30%, 60%, and 90% as the values of α. Compared to CBA, the AUC of the proposed PU learning algorithm were increased by 6.21%、11.15%、13.50% and 16.56%. Compared to POSC4.5, the AUC increased by 11.27%、15.03%、12.22%, and 7.37%. [Limitations] We did not modify the confidence of the class association rules based on the estimated proportion of positive examples. We found that the classification accuracy of the proposed PU learning algorithm gradually decreased while the value of α increased. We did not investigate the redundant rules of the CBA algorithm. [Conclusions] The proposed PU learning algorithm did better jobs than CBA and POSC4.5 algorithms.
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