Devising Asymmetric Linguistic Hedges to Enhance the Accuracy of NEFCLASS for Datasets with Highly Skewed Feature Values

Jamileh Yousefi

2020

Abstract

This paper presents a model to address the skewness problem in the NEFCLASS classifier by devising several novel asymmetric linguistic hedges within the classifier. NEFCLASS is a common example of the construction of a NEURO-FUZZY system. The NEFCLASS performs increasingly poorly as data skewness increases. This poses a challenge for the classification of biological data that commonly exhibits feature value skewness. The objective of this paper is to device several novel asymmetric linguistic hedges to modify the shape of membership functions, hence improving the accuracy of NEFCLASS. This study demonstrated that devising an appropriate asymmetric linguistic hedge significantly improves the accuracy of NEFCLASS for skewed data.

Paper Citation

in Harvard Style

Yousefi J. (2020). Devising Asymmetric Linguistic Hedges to Enhance the Accuracy of NEFCLASS for Datasets with Highly Skewed Feature Values. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: FCTA; ISBN 978-989-758-475-6, SciTePress, pages 309-320. DOI: 10.5220/0010143103090320

in Bibtex Style

@conference{fcta20,
author={Jamileh Yousefi},
title={Devising Asymmetric Linguistic Hedges to Enhance the Accuracy of NEFCLASS for Datasets with Highly Skewed Feature Values},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: FCTA},
year={2020},
pages={309-320},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010143103090320},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: FCTA
TI - Devising Asymmetric Linguistic Hedges to Enhance the Accuracy of NEFCLASS for Datasets with Highly Skewed Feature Values
SN - 978-989-758-475-6
AU - Yousefi J.
PY - 2020
SP - 309
EP - 320
DO - 10.5220/0010143103090320
PB - SciTePress