A One-Class Decision Tree Based on Kernel Density Estimation
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One-Class Classification (OCC) is a domain of machine learning which achieves training by means of a single class sample. The present work aims at developing a one-class model which addresses concerns of both performance and readability. To this end, we propose a hybrid OCC method which relies on density estimation as part of a tree-based learning algorithm. Within a greedy and recursive approach, our proposal rests on kernel density estimation to split a data subset on the basis of one or several intervals of interest. Our method shows favorable performance in comparison with common methods of the literature on a range of benchmark datasets.
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