A New Fuzzy Rule Generation Scheme based on Multiple-Selection of Influencing Factors

Automatic rule generation of fuzzy systems is a extensively studied topic which is generally handled by using a labeled dataset. This paper presents a new method for this problem which does not rely on training data. It only requests a tiny information which can be roughly acquired from any data and...

Full description

Saved in:
Bibliographic Details
Main Authors: Mutlu, Begum, Sezer, Ebru A.
Format: Conference Proceeding
Language:English
Subjects:
Fuzzy rule generation
Fuzzy sets
Fuzzy systems
Linguistics
Machine learning
Software
software fault prediction
Testing
Training data
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Automatic rule generation of fuzzy systems is a extensively studied topic which is generally handled by using a labeled dataset. This paper presents a new method for this problem which does not rely on training data. It only requests a tiny information which can be roughly acquired from any data and/or domain expert. By using this information, it selects single or multiple influencing factor(s) to determine the consequent part of rules. The experiments were performed on software fault prediction problem, and the resulting rules are compared with the rules obtained from 3 existing rule generation methods which are data-based, expert-based and partially data-based solutions. Results shows that, the proposed method is able to outperform its counterparts in most of the cases.
ISSN:1558-4739
DOI:10.1109/FUZZ-IEEE.2019.8858990