基于归一化互信息的改进特征选择

2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2015-08-18 DOI:10.1109/DCABES.2015.135

Li Yin, Xingfei Ma, Mengxi Yang, Zhao Wei, Wenqiang Gu

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引用次数: 8

摘要

对于问题(NMIFS)算法存在冗余的缺点。本文介绍了一种新的基于增强NMIFS算法的特征选择方法。在特征选择算法中引入了新的质量估计函数，克服了传统NMIFS算法的不足，并通过实验验证了该算法在归一化互信息特征选择上的有效性。实验结果表明，该算法在准确率和冗余度方面都取得了令人满意的结果。

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Improved Feature Selection Based on Normalized Mutual Information

For the question (NMIFS) algorithm has the disadvantages of redundancy. This paper introduces a new feature selection method by enhanced NMIFS algorithm. A new quality estimation function is introduced in the new feature selection algorithm to overcome the shortcomings of the classic NMIFS, and the experiment shows on that normalized mutual information feature selection The experiment shows that the INMIFS can generate impressive results in accuracy and redundancy.

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来源期刊

2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

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