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

• Published in: International journal of advances in intelligent informatics Vol. 7; no. 1; pp. 76 - 88
• Main Authors: Al-A'araji, Nabeel Hashim, Al-Mamory, Safaa Obayes, Al-Shakarchi, Ali Hashim
• Format: Journal Article
• Language: English
• Published: Yogyakarta Universitas Ahmad Dahlan 01.03.2021
• Subjects: Accuracy; Classification; Classifiers; Construction; Data mining; Datasets; Decision making; Decision trees; Experiments; Pedagogy; Probability theory; Support vector machines; Task complexity
• ISSN: 2442-6571; 2442-6571
• DOI: 10.26555/ijain.v7il.578

A large spectrum of classifiers has been described in the literature. One attractive classification technique is a Naive 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.