Finding the best diversity generation procedures for mining contrast patterns
作者：
Highlights：
• Comparison of diversity generation procedures for mining contrast patterns.
• Diversity calculated based on the amount of total, unique, and minimal patterns.
• Three new deterministic methods for generating diversity in decision trees.
• Study of the influence of data type in diversity and accuracy of methods.
• Random Forest and Bagging are the best procedures.
摘要
•Comparison of diversity generation procedures for mining contrast patterns.•Diversity calculated based on the amount of total, unique, and minimal patterns.•Three new deterministic methods for generating diversity in decision trees.•Study of the influence of data type in diversity and accuracy of methods.•Random Forest and Bagging are the best procedures.
论文关键词：Understandable classifiers,Contrast patterns,Ensemble diversity,Deterministic procedures
论文评审过程：Available online 26 February 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.02.028
