Uma abordagem visual para apoio ao aprendizado multi-instâncias

Abstract

Multiple-instance learning (MIL) is a paradigm of machine learning that aims at classifying a set (bags) of objects (instances), assigning labels only to the bags. In MIL, only the labels of bags are available for training while the labels of instances in bags are unknown. This problem is often addressed by selecting an instance to represent each bag, transforming a MIL problem into a standard supervised learning. However, there is no user support to assess this process. In this work, we propose a multi-scale tree-based visualization called MILTree that supports users in tasks related to MIL, and also two new instance selection methods called MILTree-SI and MILTree-Med to improve MIL models. MILTree is a two-level tree layout, where the first level projects bags, and the second level projects the instances belonging to each bag, allowing the user to understand the data multi-instance in an intuitive way. The developed selection methods define instance prototypes of each bag, which is important to achieve high accuracy in multi-instance classification. Both methods use the MILTree layout to visually update instance prototypes and can handle binary and multiple-class datasets. In order to classify the bags we use a SVM classifier. Moreover, with support of MILTree layout one can also update the classification model by changing the training set in order to obtain a better classifier. Experimental results validate the effectiveness of our approach, showing that visual mining by MILTree can help the users in MIL classification scenarios.