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

• Main Authors: Nabeel Hashim Al-Aaraji, Safaa Obayes Al-Mamory, Ali Hashim Al-Shakarchi
• Format: Article
• Language: English
• Published: Universitas Ahmad Dahlan 2021-03-01
• Series: IJAIN (International Journal of Advances in Intelligent Informatics)
• Online Access: http://ijain.org/index.php/IJAIN/article/view/578
• ISSN: 2442-6571; 2548-3161

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 rule-set 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.