Optimization of Naïve Bayes uses the genetic algorithm for classification data

• Published in: Journal of physics. Conference series Vol. 1918; no. 4; pp. 42039 - 42045
• Main Authors: Salim, A, Alfian, M R, Andriani, H, Afifah, N
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.06.2021
• Subjects: Accuracy; Classification; Genetic algorithms; Heuristic methods; Independent variables; Model accuracy; Optimization; Optimization techniques; Physics; Septic tanks; Statistical methods
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/1918/4/042039

Classification is one of the statistical methods to classify data systematically. However, if there is a large amount of data and various features, it often results in low accuracy. For this reason, methods are needed that can handle the data with various types. One method that can handle this problem is Naïve Bayes. Naïve Bayes is one of the methods used for classification data. This method requires a stage of selection of independent variables in increasing the accuracy of the model from Naïve Bayes. So we need an excellent method to fix these deficiencies uses a Genetic Algorithm (GA). Genetic algorithm is one of the metaheuristic methods used in optimization techniques. The data used are septic tank data in East Surabaya with eleven independent for classification data. The result of classification accuracy using Naïve Bayes is 72.7%. When Naive Bayes was used with a genetic algorithm, the classification accuracy was increased is 90.9%