Active Multi-Instance Multi-Label Learning

Abstract

Multi-instance multi-label learning (MIML) introduced by Zhou and Zhang is a comparatively new framework in machine learning with two special characteristics: Firstly, each instance is represented by a set of feature vectors (a bag of instances), and secondly, bags of instances may belong to many classes (a Multi-Label). Thus, an MIML classifier receives a bag of instances and produces a Multi-Label. For classifier training, the training set is also of this MIML structure. Labeling a data set is always cost-intensive, especially in an MIMIL framework. In order to reduce the labeling costs it is important to restructure the annotation process in such a way that the most informative examples are labeled in the beginning, and less or non-informative data more to the end of the annotation phase. Active learning is a possible approach to tackle this kind of problems in this work we focus on the MIMLSVM algorithm in combination with the k-Medoids clustering algorithm to transform the Multi-Instance to a Single-Instance representation. For the clustering distance measure we consider variants of the Hausdorff distance, namely Median- and Average-Based Hausdorff distance. Finally, active learning strategies derived from the single-instance scenario have been investigated in the MIML setting and evaluated on a benchmark data set.