Fisher’s decision tree

作者：

Highlights：

• We used real world data sets, executable file and source code available.

• Our method uses an artificial attribute to split the data set.

• As the number of features becomes higher, our method becomes more efficient.

摘要

•We used real world data sets, executable file and source code available.•Our method uses an artificial attribute to split the data set.•As the number of features becomes higher, our method becomes more efficient.

论文关键词：Oblique decision tree,Fisher’s linear discriminant,C4.5

论文评审过程：Available online 1 June 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.05.044