Decision Concept Lattice vs. Decision Trees and Random Forests

TL;DR

This paper introduces a novel supervised machine learning model that combines decision trees, ensembles, and Formal Concept Analysis, offering a polynomial-time construction method and effective classification and regression capabilities.

Contribution

It presents a new concept lattice-based model that integrates decision trees and FCA, with a polynomial-time construction algorithm and competitive prediction performance.

Findings

Polynomial-time algorithm for concept lattice construction

Effective classification and regression with comparable accuracy to state-of-the-art models

Unified approach for classification and regression tasks

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.