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Applied Intelligence

, Volume 49, Issue 11, pp 3909–3922 | Cite as

Aggregation framework for TSK fuzzy and association rules: interpretability improvement on a traffic accidents case

  • Sandra NemetEmail author
  • Dragan Kukolj
  • Gordana Ostojić
  • Stevan Stankovski
  • Dragan Jovanović
Article
  • 89 Downloads

Abstract

The number and diversity of machine learning applications causes an increasing need for understanding computational models and used data. This paper deals with a framework design of easily interpretable rules of the Takagi-Sugeno-Kang (TSK) fuzzy model. The proposed framework aggregates TSK fuzzy rules and association rules by calculating overlapping value intervals of variables appearing in both antecedent and consequent parts of fuzzy and association rules. Besides a simple insight into rule interconnections of the rule-based models, the framework provides an assessment of fuzzy rule importance, and in accordance with other rules and the complete TSK fuzzy model. The proposed framework is developed and illustrated by analysing traffic accidents with pedestrian involvement. It provides a deeper understanding of the built rule-based model, as well as more readable identification of significant accident causes. The framework can be used in many domains of analysis modelling and decision making processes where computational model understanding is crucial.

Keywords

Fuzzy rules Association rules Takagi-Sugeno-Kang fuzzy model Interpretability Traffic accident analysis Fuzzy rules visualization Feature selection 

Notes

Acknowledgements

This work was partially supported by the Ministry of Education, Science and Technology Development of the Republic of Serbia under the Grant TR32034.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.RT-RK InstituteNovi SadSerbia
  2. 2.Faculty of Technical SciencesUniversity of Novi SadNovi SadSerbia

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