loading
Documents

Research.Publish.Connect.

Paper

A Synergistic Approach to Enhance the Accuracy-interpretability Trade-off of the NECLASS Classifier for Skewed Data Distribution

Topics: Adaptive, Hierarchical, Hybrid, and Type 2 Fuzzy Systems; Fuzzy Decision Analysis, Multi-Criteria Decision Making and Decision Support; Fuzzy Information Processing, Data Bases, Information Retrieval, Big Data, Cloud Computing; Fuzzy Methods in Data Analysis, Clustering, Classification and Pattern Recognition; Fuzzy Methods in Knowledge Discovery, Machine Learning, Approximate Reasoning, Information Fusion

Authors: Jamileh Yousefi 1 ; Andrew Hamilton-Wright 2 and Charlie Obimbo 2

Affiliations: 1 Shannon School of Business, Cape Breton University, Sydney, NS and Canada ; 2 School of Computer Science, University of Guelph, Guelph, ON and Canada

ISBN: 978-989-758-384-1

ISSN: 2184-2825

Keyword(s): Fuzzy, Discretization, Neuro-fuzzy, Classification, Skewness, NEFCLASS, Rule-pruning, Adjusted residual, EQUAL-WIDTH, MME.

Abstract: NEFCLASS is a common example of a neuro-fuzzy system. The popular NEFCLASS classifier exhibits surprising behaviour when the feature values of the training and testing datasets exhibit significant skew. This paper presents a combined approach to improve the classification accuracy and interpretability of the NEFCLASS classifier, when data distribution exhibits positive skewness. The proposed model consists of two steps. Firstly, we used an alternative discretization method to initialize fuzzy sets. Secondly, we devised a statistical rule pruning algorithm based on adjusted residual to reduce the number of rules, thus improving interpretability. This method improves the interpretability of NEFCLASS without significant accuracy deterioration. Moreover, a hybrid approach combining the two approaches is developed to increase the accuracy-interpretability trade-off of NEFCLASS.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.231.220.225

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, Jamileh; Hamilton-Wright, A. and Obimbo, C. (2019). A Synergistic Approach to Enhance the Accuracy-interpretability Trade-off of the NECLASS Classifier for Skewed Data Distribution.In Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2019) ISBN 978-989-758-384-1, ISSN 2184-2825, pages 325-334. DOI: 10.5220/0008072503250334

@conference{fcta19,
author={Yousefi, Jamileh and Andrew Hamilton{-}Wright. and Charlie Obimbo.},
title={A Synergistic Approach to Enhance the Accuracy-interpretability Trade-off of the NECLASS Classifier for Skewed Data Distribution},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2019)},
year={2019},
pages={325-334},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008072503250334},
isbn={978-989-758-384-1},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2019)
TI - A Synergistic Approach to Enhance the Accuracy-interpretability Trade-off of the NECLASS Classifier for Skewed Data Distribution
SN - 978-989-758-384-1
AU - Yousefi, Jamileh
AU - Hamilton-Wright, A.
AU - Obimbo, C.
PY - 2019
SP - 325
EP - 334
DO - 10.5220/0008072503250334

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.