# Decision Concept Lattice vs. Decision Trees and Random Forests

arXiv: https://arxiv.org/abs/2106.00387 · HTML: https://mlsanity.com/paper/2106.00387/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [On interestingness measures of formal concepts](https://mlsanity.com/paper/1611.02646/) (2016)

## Built on by (most cited first, 1 of 1)

- [Conceptual Views on Tree Ensemble Classifiers](https://mlsanity.com/paper/2302.05270/) (2023)

## Abstract (arXiv)

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.