A Visual Mining Approach to Improved Multiple-Instance Learning

TL;DR

This paper introduces MILTree, a multiscale visualization tool for multiple-instance learning, along with new instance selection methods, to enhance understanding and improve classification models in MIL tasks.

Contribution

The paper presents a novel visualization technique called MILTree and two new instance selection methods specifically designed for MIL, applicable to binary and multiclass problems.

Findings

MILTree effectively supports exploring MIL datasets.

Proposed instance selection methods outperform existing alternatives.

Visual mining with MILTree improves model accuracy and understanding.

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 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 called MILTree to support MIL problems. The first level of the tree represents the bags, and the second level represents the instances belonging to each bag, allowing users to understand the MIL datasets in an intuitive way. In addition, we propose two new instance selection methods for MIL, which help users improve the model even further. Our methods can 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, which is composed of the prototype instances. Experimental results validate the effectiveness of our approach, showing that visual mining by MILTree can support exploring and improving models in MIL scenarios and that our instance selection methods outperform the currently available alternatives in most cases.