IFS-IBA similarity measure in machine learning algorithms

作者：

Highlights：

• A novel similarity measure of intuitionistic fuzzy sets (IFS) is proposed.

• The measure is based on the equivalence relation in IFS-IBA approach.

• The proposed measure is flexible and easy to interpret.

• Benefits of the measure are shown on pattern recognition and classification problems.

• IFS-IBA similarity is applied for clustering Serbian medium-sized companies.

摘要

•A novel similarity measure of intuitionistic fuzzy sets (IFS) is proposed.•The measure is based on the equivalence relation in IFS-IBA approach.•The proposed measure is flexible and easy to interpret.•Benefits of the measure are shown on pattern recognition and classification problems.•IFS-IBA similarity is applied for clustering Serbian medium-sized companies.

论文关键词：IFS-IBA approach,Intuitionistic fuzzy sets,Interpolative Boolean algebra,Similarity measure,Classification,Clustering

论文评审过程：Received 29 November 2016, Revised 17 June 2017, Accepted 28 July 2017, Available online 29 July 2017, Version of Record 2 August 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.07.048