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

Jamileh Yousefi

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

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

Topics: Neuro-Fuzzy Systems

In Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: FCTA, 309-320, 2020

Author: Jamileh Yousefi

Affiliation: Shannon School of Business, Cape Breton University, Sydney, NS, Canada

Keyword(s): Fuzzy, Discretization, NEURO-FUZZY, Classification, Skewness, NEFCLASS, EQUAL-WIDTH, MME, Linguistic Hedge, Asymmetric Hedge.

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.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 52.14.224.197

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

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) - FCTA; ISBN 978-989-758-475-6; ISSN 2184-3236, SciTePress, pages 309-320. DOI: 10.5220/0010143103090320

@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) - FCTA},
year={2020},
pages={309-320},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010143103090320},
isbn={978-989-758-475-6},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - 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
IS - 2184-3236
AU - Yousefi, J.
PY - 2020
SP - 309
EP - 320
DO - 10.5220/0010143103090320
PB - SciTePress