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In this paper, an explainable prediction model is established to select the optimum features and parameters, then the selected optimum parameters are applied to predicting potential customer churning in one foreign telecom company, discovering that the model not only achieves a desirable prediction but is also explainable through selected features, and that a balanced relation between accuracy and...
Aimed at the research on freeway detection algorithm has great significance for improving efficiency and effectiveness of freeway traffic management, this paper based on the freeway traffic flow's characteristics, in accordance with the incident detection's basic principle, researches on freeway incident detection based on Support Vector Machine (SVM). This paper designs four different simulation...
The prediction of software defect-fixing effort is important for strategic resource allocation and software quality management. Machine learning techniques have become very popular in addressing this problem and many related prediction models have been proposed. However, almost every model today faces a challenging issue of demonstrating satisfactory prediction accuracy and meaningful prediction results...
Recently ensemble classification has attracted serious attention of machine learning community as a solution for improving classification accuracy. The effect of the strategies for generating the members, combining the predictions and the size of the ensemble on the accuracy of the ensemble are of utmost interest to the researchers. In this paper, we propose and empirically evaluate a novel method...
Aimed at heart disease diagnose is an important issue and hybrid kernel functions have excellent learning ability and generalization performance, we propose SVM based on hybrid kernel function and apply the model to test the heart disease dataset. In this paper, K-type kernel function combine with linear kernel and polynomial kernel is firstly proposed, Linear combinations with different kernel functions...
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