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arXiv · 2106.00387

Decision Concept Lattice vs. Decision Trees and Random Forests

Egor Dudyrev·Sergei O. Kuznetsov
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Abstract

Decision trees and their ensembles are very popular models of supervised machine learning. In this paper we merge the ideas underlying decision trees, their ensembles and FCA by proposing a new supervised machine learning model which can be constructed in polynomial time and is applicable for both classification and regression problems. Specifically, we first propose a polynomial-time algorithm for constructing a part of the concept lattice that is based on a decision tree. Second, we describe a prediction scheme based on a concept lattice for solving both classification and regression tasks with prediction quality comparable to that of state-of-the-art models.

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Egor Dudyrev, Sergei O. Kuznetsov. 2021-06-01. Decision Concept Lattice vs. Decision Trees and Random Forests. https://arxiv.org/abs/2106.00387

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