Feature selection based on modified minimize entropy principle

Jr-Shian Chen, Hung-Lieh Chou, D. Tai
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引用次数: 1

Abstract

Feature selections have seen growing importance placed on statistics, pattern recognition, machine learning and data mining. Researchers have demonstrated the interest in the methods for improving the performance of their forecasting results. Therefore, this study proposes a feature selection approach, which based on minimize entropy principle approach. Experimental results have shown that the proposed model provided more average accuracy rate and stability then other methods.
基于改进最小熵原理的特征选择
特征选择在统计学、模式识别、机器学习和数据挖掘中越来越重要。研究人员已经表现出对提高预测结果性能的方法的兴趣。因此,本研究提出了一种基于最小熵原理的特征选择方法。实验结果表明,该模型比其他方法具有更高的平均准确率和稳定性。
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