Abstract: The traditional calibrated label ranking algorithm (calibrated label ranking, CLR) uses pairs of label associations to transform and predict results. Its algorithmic calibration is achievely comparing it with the basis of binary relevance (BR). Its prediction has a certain dependence on the results of BR, thus incurring some limitations on the prediction of some datasets. To better distinguish between the relevance and irrelevance of the label, a method is presented for calibrating label boundary regions, which further corrects the boundary portion of the relevant label and the irrelevant label using Bayesian probability, thereby improving the accuracy of the classification of the boundary domain. CLR method based on naive Bayes(NBCLRM) presented is compared with seven traditional methods such as calibrated label ranking. Experimental results show that the proposed algorithm can not only adjust prediction results by modifying the thresholds ε and μ, but also effectively improve the performance of traditional multi-label learning methods.

Key words: data mining, Naive Bayes, calibrated label ranking, multi-label learning algorithm