Maximum margin multiple-instance feature weighting

Feature weighting is of considerable importance in machine learning due to its effectiveness to highlight relevant components and suppress irrelevant ones. In this paper, we focus on the feature weighting problem in a specific machine learning area: multiple-instance learning, and propose maximum margin multiple-instance feature weighting (M3IFW) to seek large classification margins in the weighted feature space. The designed M3IFW algorithm can be applied to both standard binary-class multiple-instance learning and the corresponding multi-class learning, and we abbreviate them to B-M3IFW (binary-class M3IFW) and M-M3IFW (multi-class M3IFW), respectively. Both B-M3IFW and M-M3IFW contain three kinds of unknown variables, i.e., positive prototypes, classification margins, and weighting coefficients. We utilize the coordinate ascent algorithm to update the three kinds of unknown variables, respectively and iteratively, and then perform classifications in the weighted feature space. Experiments conducted on synthetic and real-world datasets empirically demonstrate the effectiveness of M3IFW in improving classification accuracies.