Perfomance analysis of Naive Bayes method with data weighting
Keywords:Naïve Bayes, Gain Ratio, Air Quality, Water Quality, Accuracy.
Classification using naive bayes algorithm for air quality dataset has an accuracy rate of 39.97%. This result is considered not good and by using all existing data attributes. By doing pre-processing, namely feature selection using the gain ratio algorithm, the accuracy of the Naive Bayes algorithm increases to 61.76%. This proves that the gain ratio algorithm can improve the performance of the naive bayes algorithm for air quality dataset classification. Classification using naive bayes algorithm for air quality dataset. While the Water Quality dataset has an accuracy rate of 93.18%. These results are considered good and by using all the existing data attributes. By doing pre-processing, namely feature selection using the gain ratio algorithm, the accuracy of the Naive Bayes algorithm increases to 95.73%. This proves that the gain ratio algorithm can improve the performance of the naive bayes algorithm for air quality dataset classification. Classification using Naive Bayes algorithm for Water Quality dataset. Based on the tests that have been carried out on all data, it can be seen that the Weight nave Bayes classification model can provide better accuracy values because there is a change in the weighting of the attribute values in the dataset used. The value of the weighted Gain ratio is used to calculate the probability in Nave Bayes, which is a parameter to see the relationship between each attribute in the data, and is used as the basis for the weighting of each attribute of the dataset. The higher the Gain ratio of an attribute, the greater the relationship to the data class. So that the accuracy value increases than the accuracy value generated by the Naïve Bayes classification model. The increase in accuracy in the Naïve Bayes classification model is due to the number of weights from the attribute selection in the Gain ratio.
Tan, P.-N., Steinbach, M., & Kumar, V. (2013 ). Introduction to Data Mining. Boston: Pearson Addison-Wesley.
Bustami, B. (2014). PENERAPAN ALGORITMA NAIVE BAYES UNTUK MENGKLASIFIKASI DATA NASABAH ASURANSI. Jurnal Informatika (JIFO), 884-898.
Gorunescu, F. (2011). Data Mining: Concepts, Models and Techniques. Heidelberg: Springer Berlin.
Mao, X., Zhao, G., & Sun, R. (2017). Naive Bayesian algorithm classification model with local attribute weighted based on KNN. IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE.
Niloy, N., & Navid, M. (2018). Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients. American Journal of Data Mining and Knowledge Discovery, 1-12.
Patil, T. R., & Sherekar, S. S. (2013). Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification. International Journal Of Computer Science And Applications, 256-261.
Raviya, K. H., & Gajjar, B. (2013). Performance Evaluation of Different Data Mining Classification Algorithm Using WEKA. PARIPEX - INDIAN JOURNAL OF RESEARCH, 19-21.
Raymond, C., & Jason, O. (2011). General Chemistry: The Essential Concepts Sixth Edition. New York: McGraw-Hill .
Sahu, M., Nagwani, N. K., Verma, S., & Shirke, S. (2015). Performance Evaluation of Different Classifier for Eye State Prediction Using EEG Signal. International Journal of Knowledge Engineering (IJKE), 141.
Wang, Q. (2014). A Hybrid Sampling SVM Approach to Imbalanced Data Classification. Abstract and Applied Analysis, 1-7.
Witten, I. E. (2005). Data Mining Practical Machine Learning Tools and Techniques. 2nd Edition. San Francisco: Morgan Kaufmann Publishers.
Yuliana , Y., & Erlangga, E. (2017). Analysis Of Data Mining Methods Naive Bayes Classifier (NBC). International Conference on Engineering and Technology Development (ICETD) (pp. 246-260). Bandar Lampung: Information System, Computer Science Faculty, Bandar Lampung University.
Zhang, H., & Wang, Z. (2011). A normal distribution-based over-sampling approach to imbalanced data classification. ADMA'11: Proceedings of the 7th international conference on Advanced Data Mining and Applications (pp. 83–96). Beijing China: ADMA.
How to Cite
Copyright (c) 2022 Afdhaluzzikri Afdhaluzzikri, Herman Mawengkang, Opim Salim Sitompul
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.