A data sampling and attribute selection strategy for improving decision tree construction
作者：
Highlights：
• Decision trees construction outputs suffer from over-fitting and complexity problems.
• Attribute selection and data sampling combination aims to overcome construction problems.
• This paper presents a PSO approach to select an optimal attribute and sample combination.
• The optimal selected solution helps in avoiding over-fitting and complexity problems.
• The empirical results clearly show that the proposed approach shows good solutions.
摘要
•Decision trees construction outputs suffer from over-fitting and complexity problems.•Attribute selection and data sampling combination aims to overcome construction problems.•This paper presents a PSO approach to select an optimal attribute and sample combination.•The optimal selected solution helps in avoiding over-fitting and complexity problems.•The empirical results clearly show that the proposed approach shows good solutions.
论文关键词：Decision tree,Sampling,Attribute selection,Particle swarm optimization,Instantaneous angular seed,Fault diagnosis
论文评审过程：Received 24 November 2018, Revised 30 March 2019, Accepted 30 March 2019, Available online 4 April 2019, Version of Record 6 April 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.03.052
