Learning Semantically Coherent Rules
نویسندگان
چکیده
The capability of building a model that can be understood and interpreted by humans is one of the main selling points of symbolic machine learning algorithms, such as rule or decision tree learners. However, those algorithms are most often optimized w.r.t. classification accuracy, but not the understandability of the resulting model. In this paper, we focus on a particular aspect of understandability, i.e., semantic coherence. We introduce a variant of a separate-and-conquer rule learning algorithm using a WordNet-based heuristic to learn rules that are semantically coherent. In an evaluation on di↵erent datasets, we show that the approach learns rules that are significantly more semantically coherent, without losing accuracy.
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