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RuleKit: A comprehensive suite for rule-based learning
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DOI：10.1016/j.knosys.2020.105480.png)
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Abstract
En 中文
Rule-based models are often used for data analysis as they combine interpretability with predictive power. We present RuleKit, a versatile tool for rule learning. Based on a sequential covering induction algorithm, it is suitable for classification, regression, and survival problems. The presence of a user-guided induction facilitates verifying hypotheses concerning data dependencies which are expected or of interest. The powerful and flexible experimental environment allows straightforward investigation of different induction schemes. The analysis can be performed in batch mode, through RapidMiner plug-in, or R package. (C) 2020 The Authors. Published by Elsevier B.V.
Keywords:
Rule learning
Classification
Regression
Survival analysis
User-guided induction
Knowledge discovery
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