ExMo: Explainable AI Model using Inverse Frequency Decision Rules

TL;DR

ExMo is an interpretable machine learning model that uses TF-IDF-based decision rules to improve accuracy and provide human-friendly explanations, outperforming Bayesian Rule List in various datasets.

Contribution

The paper introduces ExMo, a novel method for extracting decision rules using TF-IDF features, enhancing interpretability and accuracy over existing rule-based models.

Findings

ExMo achieves 20% higher accuracy than BRL.

ExMo's explanations are easily understandable by non-experts.

ExMo performs comparably to deep learning models in accuracy.

Abstract

In this paper, we present a novel method to compute decision rules to build a more accurate interpretable machine learning model, denoted as ExMo. The ExMo interpretable machine learning model consists of a list of IF...THEN... statements with a decision rule in the condition. This way, ExMo naturally provides an explanation for a prediction using the decision rule that was triggered. ExMo uses a new approach to extract decision rules from the training data using term frequency-inverse document frequency (TF-IDF) features. With TF-IDF, decision rules with feature values that are more relevant to each class are extracted. Hence, the decision rules obtained by ExMo can distinguish the positive and negative classes better than the decision rules used in the existing Bayesian Rule List (BRL) algorithm, obtained using the frequent pattern mining approach. The paper also shows that ExMo learns a qualitatively better model than BRL. Furthermore, ExMo demonstrates that the textual explanation can be provided in a human-friendly way so that the explanation can be easily understood by non-expert users. We validate ExMo on several datasets with different sizes to evaluate its efficacy. Experimental validation on a real-world fraud detection application shows that ExMo is 20% more accurate than BRL and that it achieves accuracy similar to those of deep learning models.