A Visual Mining Approach to Improved Multiple-Instance Learning

• URL: http://arxiv.org/abs/2012.07257v1
• Date: Mon, 14 Dec 2020 05:12:43 GMT
• Title: A Visual Mining Approach to Improved Multiple-Instance Learning
• Authors: Sonia Castelo, Moacir Ponti, Rosane Minghim
• Abstract summary: Multiple-instance learning (MIL) is a paradigm of machine learning that aims to classify a set (bag) of objects (instances) and assign labels only to the bags. We propose a multiscale tree-based visualization to support MIL. The first level of the tree represents the bags, and the second level represents the instances belonging to each bag.
• Score: 3.611492083936225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple-instance learning (MIL) is a paradigm of machine learning that aims to classify a set (bag) of objects (instances), assigning labels only to the bags. This problem is often addressed by selecting an instance to represent each bag, transforming a MIL problem into a standard supervised learning. Visualization can be a useful tool to assess learning scenarios by incorporating the users' knowledge into the classification process. Considering that multiple-instance learning is a paradigm that cannot be handled by current visualization techniques, we propose a multiscale tree-based visualization to support MIL. The first level of the tree represents the bags, and the second level represents the instances belonging to each bag, allowing the user to understand the data in an intuitive way. In addition, we propose two new instance selection methods for MIL, which help the user to improve the model even further. Our methods are also able to handle both binary and multiclass scenarios. In our experiments, SVM was used to build the classifiers. With support of the MILTree layout, the initial classification model was updated by changing the training set - composed by the prototype instances. Experimental results validate the effectiveness of our approach, showing that visual mining by MILTree can help users in exploring and improving models in MIL scenarios, and that our instance selection methods over-perform current available alternatives in most cases.

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