Pattern classification using tolerance rough sets based on non-additive grey relational analysis and DEMATEL

Abstract

Purpose

It is challenging to derive an appropriate tolerance relation for tolerance rough set-based classifiers (TRSCs). The traditional tolerance rough set employs a simple distance function to determine the tolerance relation. However, such a simple function does not take into account criterion weights and the interaction among criteria. Further, the traditional tolerance relation ignores interdependencies concerning direct and indirect influences among patterns. This study aimed to incorporate interaction and interdependencies into the tolerance relation to develop non-additive grey TRSCs (NG-TRSCs).

Design/methodology/approach

For pattern classification, this study applied non-additive grey relational analysis (GRA) and the decision-making trial and evaluation laboratory (DEMATEL) technique to solve problems arising from interaction and interdependencies, respectively.

Findings

The classification accuracy rates derived from the proposed NG-TRSC were compared to those of other TRSCs with distinctive features. The results showed that the proposed classifier was superior to the other TRSCs considered.

Practical implications

In addition to pattern classification, the proposed non-additive grey DEMATEL can further benefit the applications for managerial decision-making because it simplifies the operations for decision-makers and enhances the applicability of DEMATEL.

Originality/value

This paper contributes to the field by proposing the non-additive grey tolerance rough set (NG-TRS) for pattern classification. The proposed NG-TRSC can be constructed by integrating the non-additive GRA with DEMATEL by using a genetic algorithm to determine the relevant parameters.

Citation

Jiang, P., Wang, W., Hu, Y.-C., Chiu, Y.-J. and Tsao, S.-J. (2021), "Pattern classification using tolerance rough sets based on non-additive grey relational analysis and DEMATEL", Grey Systems: Theory and Application, Vol. 11 No. 1, pp. 166-182. https://doi.org/10.1108/GS-05-2020-0062