Decision Concept Lattice vs. Decision Trees and Random Forests
This work introduces a novel model that could enhance interpretability and efficiency in machine learning, though it appears incremental as it builds on existing methods like decision trees and FCA.
The authors tackled the problem of supervised learning by merging decision trees, ensembles, and Formal Concept Analysis (FCA) to propose a new model that constructs a concept lattice in polynomial time for classification and regression tasks, achieving prediction quality comparable to state-of-the-art models.
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.