Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Multi-stage mixed rule learning approach for advancing performance of rule-based classification

Liu, Han and Chen, Shyi-Ming 2019. Multi-stage mixed rule learning approach for advancing performance of rule-based classification. Information Sciences 495 , pp. 65-77. 10.1016/j.ins.2019.05.008
Item availability restricted.

[img] PDF - Accepted Post-Print Version
Restricted to Repository staff only until 4 May 2020 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (767kB)

Abstract

Rule learning is a special type of machine learning approaches, and its key advantage is the generation of interpretable models, which provides a transparent process of showing how an input is mapped to an output. Traditional rule learning algorithms are typically based on Boolean logic for inducing rule antecedents, which are very effective for training models on data sets that involve discrete attributes only. When continuous attributes are present in a data set, traditional rule learning approaches need to employ crisp intervals. However, in reality, problems usually show shades of grey, which motivated the development of fuzzy rule learning approaches by employing fuzzy intervals for handling continuous attributes. While a data set contains a large portion of discrete attributes or even no continuous attributes, fuzzy approaches cannot be used to learn rules effectively, leading to a drop in the performance. In this paper, a multi-stage approach of mixed rule learning is proposed, which involves strategic combination of both traditional and fuzzy approaches to handle effectively various types of attributes. We compare our proposed approach with existing algorithms of rule learning. Our experimental results show that our proposed approach leads to significant advances in the performance compared with the existing algorithms.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Elsevier
ISSN: 0020-0255
Date of First Compliant Deposit: 14 May 2019
Date of Acceptance: 3 May 2019
Last Modified: 30 Jun 2019 21:50
URI: http://orca.cf.ac.uk/id/eprint/122359

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics