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Author: search.filters.author.Jaiyen, SaichonSubject: search.filters.subject.Decision Tree

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    Bank direct marketing analysis of asymmetric information based on machine learning
    (2015-08-24)
    Ruangthong, Pumitara
    ;
    Jaiyen, Saichon
    The bank direct marketing campaign for offering products that meet the customers' needs is the challenge problems. The bank direct marketing data analysis is important work that helps the banks predict whether customers will sign a long term deposits with the banks. The method that can predict such customers' needs can be profitable to the banks for improving their marketing campaign strategies. Unfortunately, it is very hard to predict the customers' needs because the available information is asymmetric. In this paper, we propose the method to analyze asymmetric information using SMOTE algorithm and Rotation Forest (PCA)-J48. The SMOTE method is used to modify the data and improve the accuracy of the prediction. The performance of the proposed method is evaluated and compared to Decision Tree, Rotation Forest, Navie Bayes, BayesNet, Multilayer Perceptron Neural Network, RBF Neural Network. The experimental results show the predicting accuracies of all predictors. The experiments show that Rotation Forest (PCA)-J48 can achieve the highest value of accuracy and specificity. However, the sensitivity of Rotation Forest (PCA)-J48 is higher than all methods except BayesNet and Rotation Forest (PCA) RandomTree.
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    A hybrid ensemble of machine and statistical learning using confidence-based boosting
    (2015-01-01)
    Chairatanasongporn, Nattawut
    ;
    Jaiyen, Saichon
    Nowadays, the classification problems have become more challenging due to the various types of data set. Some data are appropriated for machine learning techniques and some data are appropriated for statistical leaning techniques. This work proposes a new hybrid ensemble of machine and statistical learning models using confidence-based boosting. The proposed method which uses variants of based classifiers can solve classification problems in variant data set. Moreover, combining the confidence value to the current boosting method can improve the performance of classification. The performance of proposed method is compared to the ensemble of decision trees and MRN created by Adaboost.M1 on data sets from UCI. The experimental results show that the proposed method can improve the accuracy in both binary and multiclass classification problems.