Boolean Decision Rules via Column Generation

05/24/2018
by   Sanjeeb Dash, et al.
0

This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification. An integer program is formulated to optimally trade classification accuracy for rule simplicity. Column generation (CG) is used to efficiently search over an exponential number of candidate clauses (conjunctions or disjunctions) without the need for heuristic rule mining. This approach also bounds the gap between the selected rule set and the best possible rule set on the training data. To handle large datasets, we propose an approximate CG algorithm using randomization. Compared to three recently proposed alternatives, the CG algorithm dominates the accuracy-simplicity trade-off in 7 out of 15 datasets. When maximized for accuracy, CG is competitive with rule learners designed for this purpose, sometimes finding significantly simpler solutions that are no less accurate.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/16/2021

Interpretable and Fair Boolean Rule Sets via Column Generation

This paper considers the learning of Boolean rules in either disjunctive...
research
06/05/2019

Generalized Linear Rule Models

This paper considers generalized linear models using rule-based features...
research
03/04/2021

Learning Accurate and Interpretable Decision Rule Sets from Neural Networks

This paper proposes a new paradigm for learning a set of independent log...
research
11/23/2015

Interpretable Two-level Boolean Rule Learning for Classification

This paper proposes algorithms for learning two-level Boolean rules in C...
research
01/24/2023

Efficient learning of large sets of locally optimal classification rules

Conventional rule learning algorithms aim at finding a set of simple rul...
research
12/22/2016

Role of Simplicity in Creative Behaviour: The Case of the Poietic Generator

We propose to apply Simplicity Theory (ST) to model interest in creative...

Please sign up or login with your details

Forgot password? Click here to reset