Learning on Probabilistic Labels
Peng Peng, Raymond Chi-Wing Wong, Philip S. Yu · 2014
Classification is a fundamental topic in the literature of data mining and all recent hot topics like active learning and transfer learning all rely on the concept of classification. Probabilistic information becomes more prevalent nowadays and can be found easily in many applications like crowdsourcing and pattern recognition. In this paper, we focus on a dataset which contains probabilistic information for classification. Based on this probabilistic dataset, we propose a classifier and give a theoretical bound linking the error rate of our classifier and the number of instances needed for training. Interestingly, we find that our theoretical bound is asymptotically at least no worse than the previously best-known bounds developed based on the traditional dataset. Furthermore, our classifier guarantees a fast rate of convergence compared with traditional classifiers. Experimental results show that our proposed algorithm has a higher accuracy than the traditional algorithm. We believe that this work is influential since it opens a new topic on the probabilistic dataset, allowing researchers to study all topics related to classification like active learning and transfer learning under this new probabilistic setting.