Abstract

The ease of interpretation of a classification model is essential for the task of validating it. Sometimes it is required to clearly explain the classification process of a model’s predictions. Models which are inherently easier to interpret can be effortlessly related to the context of the problem, and their predictions can be, if necessary, ethically and legally evaluated. In this paper, we propose a novel method to generate rule-based classifiers from categorical data that can be readily interpreted. Classifiers are generated using a multi-objective optimization approach focusing on two main objectives: maximizing the performance of the learned classifier and minimizing its number of rules. The multi-objective evolutionary algorithms ENORA and NSGA-II have been adapted to optimize the performance of the classifier based on three different machine learning metrics: accuracy, area under the ROC curve, and root mean square error. We have extensively compared the generated classifiers using our proposed method with classifiers generated using classical methods such as PART, JRip, OneR and ZeroR. The experiments have been conducted in full training mode, in 10-fold cross-validation mode, and in train/test splitting mode. To make results reproducible, we have used the well-known and publicly available datasets Breast Cancer, Monk’s Problem 2, Tic-Tac-Toe-Endgame, Car, kr-vs-kp and Nursery. After performing an exhaustive statistical test on our results, we conclude that the proposed method is able to generate highly accurate and easy to interpret classification models.

Highlights

  • Supervised Learning is the branch of Machine Learning (ML) [1] focused on modeling the behavior of systems that can be found in the environment

  • Deep Learning Neural Networks (DLNN) are capable of modeling very complex behaviors, but it is extremely difficult to understand the logic behind their predictions, and similar considerations can be drawn for SVNs and Instance-based Learning (IBL), the underlying principles are different

  • As for the Breast Cancer dataset, in the optimization model driven by NSGA-II, with root mean square error as the first objective, only PART was able to achieve similar results, slightly worse, but at the price of having 15 rules, making the system clearly not interpretable

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Summary

Introduction

Supervised Learning is the branch of Machine Learning (ML) [1] focused on modeling the behavior of systems that can be found in the environment. DLNNs are capable of modeling very complex behaviors, but it is extremely difficult to understand the logic behind their predictions, and similar considerations can be drawn for SVNs and IBLs, the underlying principles are different. These models are known as black-box methods. RBCs are classification learning systems that achieve a high level of interpretability because they are based on a human-like logic. Appendix C shows the symbols and the nomenclature used in the paper

Multi-Objective Constrained Optimization
The Multi-Objective Evolutionary Algorithms ENORA and NSGA-II
14: Evaluate Offspring2
Rule-Based Classification for Categorical Data
A Multi-Objective Optimization Solution
Representation
Initial Population
Fitness Functions
Variation Operators
Experiment and Results
The Breast Cancer Dataset
The Monk’s Problem 2 Dataset
Optimization Models
Choosing the Best Pareto Front
Method
Additional Experiments
Analysis of Results and Discussion
Conclusions and Future Works
Full Text
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