Constructing decision rules from naive bayes model for robust and low complexity classification

Abstract

Article history Selected paper from The 2020 Global Research Conference (GRaCe'20), Trengganu-Malaysia (Virtually), 16-18 October 2020, https://terengganu.uitm.edu.my/ grace2020/. Peer-reviewed by GRaCe'20 Scientific Committee and Editorial Team of IJAIN journal. Received October 26, 2020 Revised November 10, 2020 Accepted March 15, 2021 Available online March 31, 2021 A large spectrum of classifiers has been described in the literature. One attractive classification technique is a Naïve Bayes (NB) which has been relayed on probability theory. NB has two major limitations: First, it requires to rescan the dataset and applying a set of equations each time to classify instances, which is an expensive step if a dataset is relatively large. Second, NB may remain challenging for non-statisticians to understand the deep work of a model. On the other hand, Rule-Based classifiers (RBCs) have used IF-THEN rules (henceforth, rule-set), which are more comprehensible and less complex for classification tasks. For elevating NB limitations, this paper presents a method for constructing a rule-set from the NB model, which serves as RBC. Experiments of the constructing ruleset have been conducted on (Iris, WBC, Vote) datasets. Coverage, Accuracy, M-Estimate, and Laplace are crucial evaluation metrics that have been projected to rule-set. In some datasets, the rule-set obtains significant accuracy results that reach 95.33 %, 95.17% for Iris and vote datasets, respectively. The constructed rule-set can mimic the classification capability of NB, provide a visual representation of the model, express rules infidelity with acceptable accuracy; an easier method to interpreting and adjusting from the original model. Hence, the rule-set will provide a comprehensible and lightweight model than NB itself.