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Classification Based on Multivariate Contrast Patterns
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DOI：10.1109/ACCESS.2019.2913649.png)
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Abstract
En 中文
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
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