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    Comparison of Machine Learning Methods for Binary Classification of Multicollinearity Data
    (2024-12-02)
    Araveeporn, Autcha
    ;
    Wanitjirattikal, Puntipa
    This study examines the effectiveness of binary classification performance in multicollinearity. Four machine learning methods, namely backpropagation neural network, Naïve Bayes, support vector machine, and random forest, are compared in terms of their efficiency in handling multicollinear data. The evaluation of binary classification performance efficiency considers multicollinearity in independent variables, considering both a constant correlation model and the Toeplitz correlation. Correlation coefficients of 0.1 and 0.9 are explored in the analysis. The independent variables in this study are simulated from a multivariate normal distribution with 10, 20, 30, and 40 variables, respectively. The dependent variable is constructed using the logit function with sample sizes of 100 and 200. The simulation and data analysis are performed using the R Studio program and repeated 1,000 times for each scenario. The findings of this research reveal that the backpropagation neural network and Naïve Bayes methods exhibit superior performance in determining the mean accuracy percentage under constant correlation. On the other hand, the backpropagation neural network and support vector machine are the most effective methods in determining the mean accuracy percentage when dealing with multicollinearity in the form of Toeplitz correlation.
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    Integrating data mining techniques for naïve bayes classification: Applications to medical datasets
    (2021-09-01)
    Changpetch, Pannapa
    ;
    Pitpeng, Apasiri
    ;
    Hiriote, Sasiprapa
    ;
    Yuangyai, Chumpol
    In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve bayes classifier—were combined to improve the per-formance of the latter. A classification tree was used to discretize quantitative predictors into cate-gories and ASA was used to generate interactions in a fully realized way, as discretized variables and interactions are key to improving the classification accuracy of the naïve Bayes classifier. We applied our methodology to three medical datasets to demonstrate the efficacy of the proposed method. The results showed that our methodology outperformed the existing techniques for all the illustrated datasets. Although our focus here was on medical datasets, our proposed methodology is equally applicable to datasets in many other areas.
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    Comparative Study of Prediction Models for Final GPA Score: A Case Study of Rajabhat Rajanagarindra University
    (2018-09-14)
    Putpuek, Narongsak
    ;
    Rojanaprasert, Natcha
    ;
    Atchariyachanvanich, Kanokwan
    ;
    Thamrongthanyawong, Thananya
    Recently, the analysis of educational data has become important to all universities. Rajabhat Rajanagarindra University, Thailand, wanted to study and analyze the students' performance based on their personal background. Thus, this research aimed to compare prediction models for the level of the final grade point average (GPA) score of graduated students using the data from the Faculty of Education during the 2010 to 2012 academic years. Two decision tree (C4.5 and ID3) algorithms, plus Naïve Bayes and K-nearest neighbor data mining techniques were adopted to analyze the data according to the CRISP-DM process. Factors that were proposed to influence the graduation GPA include the student's gender, scholarship awarded, previous educational background, admission type, talent and province of high school. The analysis revealed that the Naïve Bayes algorithm gave the best overall accuracy of 43.18%. This could help predict the graduation GPA score of students in the future and support teachers to make educational advice for their students and to develop the student quality in the future.