Learning from label proportions with pinball loss

Learning from label proportions is a new kind of learning problem which has drawn much attention in recent years. Different from the well-known supervised learning, it considers instances in bags and uses the label proportion of each bag instead of instance. As obtaining the instance label is not always feasible, it has been widely used in areas like modeling voting behaviors and spam filtering. However, learning from label proportions still suffers great challenges due to the inference of noise, the improper partition of bags and so on. In this paper, we propose a novel learning from label proportions method based on pinball loss, called “pSVM-pin”, to address the above issues. The pinball loss is introduced to generate an effective classifier in order to eliminate the impact of noise. Experimental results prove the precision of pSVM-pin compared with competing methods.