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• W2944557777 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." @default.
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• W2944557777 date "2019-01-01" @default.
• W2944557777 modified "2023-10-14" @default.
• W2944557777 title "Classification Based on Multivariate Contrast Patterns" @default.
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• W2944557777 doi "https://doi.org/10.1109/access.2019.2913649" @default.
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