Classification Based on Multivariate Contrast Patterns

Abstract

There is a growing interest in the development of classifiers based on contrast patterns (CPs); partly due to the advantage of them being able to explain classification results in a language that is easy to understand for an expert. CP-based classifiers, when using contrast patterns extracted by miners based on decision trees, attain accuracies comparable with other state-of-the-art classifiers. The existing decision tree-based miners use univariate decision trees (UDTs) to extract CPs. In this paper, we define the concept of multivariate CP. We introduce a multivariate CP miner based on multivariate decision trees (MDTs) as well as a new filtering algorithm for multivariate CPs. From our experimental results, we conclude that our proposed CP miner allows obtaining significantly better classification results than the other state-of-the-art classifiers.

Keywords

• Supervised classification
• pattern-based classification
• multivariate decision trees
• comprehensible classifiers