IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Decision Making by a Multiple-Rule Classifier: The Role of Rule Qualities

Decision Making by a Multiple-Rule Classifier: The Role of Rule Qualities
View Sample PDF
Author(s): Ivan Bruha (McMaster University, Canada)
Copyright: 2008
Pages: 8
Source title: Encyclopedia of Decision Making and Decision Support Technologies
Source Author(s)/Editor(s): Frederic Adam (University College Cork, Ireland)and Patrick Humphreys (London School of Economics, UK)
DOI: 10.4018/978-1-59904-843-7.ch020

Purchase

View Decision Making by a Multiple-Rule Classifier: The Role of Rule Qualities on the publisher's website for pricing and purchasing information.

Abstract

A rule-inducing learning algorithm yields a set of decision rules that depict knowledge discovered from a (usually large) dataset; therefore, this topic is often known as knowledge discovery from databases (KDD). Any classifier (or, expect system) then can utilize this decision set to derive a decision about given problems, observations, or diagnostics. The decision set (induced by a learning algorithm) may be either of the form of an ordered or unordered set of rules. The latter seems to be more understandable by humans and directly applicable in most expert systems, or generally, any decision- supporting one. However, classification utilizing the unordered-mode decision set may be accompanied by some conflict situations, particularly when several rules belonging to different classes match (are satisfied by, “fire” for) an input to-be-classified (unseen) object. One of the possible solutions to this conflict is to associate each decision rule induced by a learning algorithm with a numerical factor, which is commonly called the rule quality (An & Cercone, 2001; Bergadano et al., 1988; Bruha, 1997; Kononenko, 1992; Mingers, 1989; Tkadlec & Bruha, 2003). This article first briefly introduces the underlying principles for defining rules qualities, including statistical tools such as contingency tables and then surveys empirical and statistical formulas of the rule quality and compares their characteristics. Afterwards, it presents an application of a machine learning algorithm utilizing various formulas of the rule qualities in medical area.

Related Content

Yu Bin, Xiao Zeyu, Dai Yinglong. © 2024. 34 pages.
Liyin Wang, Yuting Cheng, Xueqing Fan, Anna Wang, Hansen Zhao. © 2024. 21 pages.
Tao Zhang, Zaifa Xue, Zesheng Huo. © 2024. 32 pages.
Dharmesh Dhabliya, Vivek Veeraiah, Sukhvinder Singh Dari, Jambi Ratna Raja Kumar, Ritika Dhabliya, Sabyasachi Pramanik, Ankur Gupta. © 2024. 22 pages.
Yi Xu. © 2024. 37 pages.
Chunmao Jiang. © 2024. 22 pages.
Hatice Kübra Özensel, Burak Efe. © 2024. 23 pages.
Body Bottom