标题： 决策概念格与决策树和随机森林 显示英文标题

标题： Decision Concept Lattice vs. Decision Trees and Random Forests

摘要： 决策树及其集成在监督机器学习中是非常流行的模型。 在本文中，我们结合了决策树、其集成和FCA的思路，提出了一种新的监督机器学习模型，该模型可以在多项式时间内构建，并适用于分类和回归问题。 具体来说，我们首先提出了一种多项式时间算法，用于构建基于决策树的概念格的一部分。 其次，我们描述了一种基于概念格的预测方案，用于解决分类和回归任务，其预测质量与最先进的模型相当。 显示英文摘要

摘要： 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.