Dispersion Ratio based Decision Tree Model for Classification

作者：

Highlights：

• Here Dispersion Ratio is proposed to identify splitting attribute in Decision Trees.

• The proposed method works for any type of variable: categorical, nominal, continuous.

• Efficient discretization method is proposed for continuous features.

• Has no bias towards features with more distinct values like many other methods.

• Extensive evaluation and analysis is performed on a large number of datasets.

摘要

•Here Dispersion Ratio is proposed to identify splitting attribute in Decision Trees.•The proposed method works for any type of variable: categorical, nominal, continuous.•Efficient discretization method is proposed for continuous features.•Has no bias towards features with more distinct values like many other methods.•Extensive evaluation and analysis is performed on a large number of datasets.

论文关键词：Data Mining,Decision Tree,Information Gain,Correlation Ratio,Dispersion_Ratio,Classification

论文评审过程：Received 6 April 2018, Revised 10 August 2018, Accepted 20 August 2018, Available online 24 August 2018, Version of Record 7 September 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.08.039