Cost-Sensitive Multi-Label Classification with Applications

• Main Authors: Hung-Yi Lo, 駱宏毅
• Other Authors: 林守德
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/61015886145358618517

博士 === 國立臺灣大學 === 資訊工程學研究所 === 101 === We study a generalization of the traditional multi-label classification, which we refer to as cost-sensitive multi-label classification (CSML). In this problem, the misclassification cost can be different for each instance-label pair. For solving the problem, we propose two novel and general strategies based on the problem transformation technique. The proposed strategies transform the CSML problem to several cost-sensitive single-label classification problems. In addition, we propose a basis expansion model for CSML, which we call the Generalized k-Labelsets Ensemble (GLE). In the basis expansion model, a basis function is a label powerset classifier trained on a random k-labelset. The expansion coefficients are learned by minimizing the cost-weighted global error between the prediction and the ground truth. GLE can also be used for traditional multi-label classification. Experimental results on both multi-label classification and cost-sensitive multi-label classification demonstrate that our method has better performance than other methods. Cost-sensitive classification is based on the assumption that the cost is given according to the application. “Where does cost come from?” is an important practical issue. We study two real-world prediction tasks and link their data distribution to the cost information. The two tasks are medical image classification and social tag prediction. In medical image classification, we observe a patient-imbalanced phenomenon that has seriously hurt the generalization ability of the image classifier. We design several patient-balanced learning algorithms based on cost-sensitive binary classification. The success of our patient-balanced learning methods has been proved by winning KDD Cup 2008. For social tag prediction, we propose to treat the tag counts as the mis-classification costs and model the social tagging problem as a cost-sensitive multi-label classification problem. The experimental results in audio tag annotation and retrieval demonstrate that the CSML approaches outperform our winning method in Music Information Retrieval Evaluation eXchange (MIREX) 2009 in terms of both cost-sensitive and cost-less evaluation metrics. The results on social bookmark prediction also demonstrate that our proposed method has better performance than other methods.