Decision Concept Lattice vs. Decision Trees and Random Forests
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.
Keywords
Cite
@article{arxiv.2106.00387,
title = {Decision Concept Lattice vs. Decision Trees and Random Forests},
author = {Egor Dudyrev and Sergei O. Kuznetsov},
journal= {arXiv preprint arXiv:2106.00387},
year = {2021}
}
Comments
8 pages, 2 figures. The final authenticated version is going to be published in Braud, A., Buzmakov, A., Hanika, T., Le Ber, F. (eds.) ICFCA 2021. LNCS (LNAI), vol. 12733, pp. 1-9. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-030-77867-5_16