Interpretable Rule Discovery Through Bilevel Optimization of Split-Rules of Nonlinear Decision Trees for Classification Problems
IEEE Transactions on Cybernetics(2021)
摘要
For supervised classification problems involving design, control, and other practical purposes, users are not only interested in finding a highly accurate classifier but they also demand that the obtained classifier be easily interpretable. While the definition of interpretability of a classifier can vary from case to case, here, by a humanly interpretable classifier, we restrict it to be expresse...
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关键词
Optimization,Task analysis,Support vector machines,Complexity theory,Decision trees,Machine learning algorithms,Evolutionary computation
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