Inexact Field Learning Approach for Data Mining

Inexact Field Learning Approach for Data Mining

Honghua Dai (Deakin University, Australia)
Copyright: © 2005 |Pages: 4
DOI: 10.4018/978-1-59140-557-3.ch115
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Inexact fielding learning (IFL) (Ciesieski & Dai, 1994; Dai & Ciesieski, 1994a, 1994b, 1995, 2004; Dai & Li, 2001) is a rough-set, theory-based (Pawlak, 1982) machine learning approach that derives inexact rules from fields of each attribute. In contrast to a point-learning algorithm (Quinlan, 1986, 1993), which derives rules by examining individual values of each attribute, a field learning approach (Dai, 1996) derives rules by examining the fields of each attribute. In contrast to exact rule, an inexact rule is a rule with uncertainty. The advantage of the IFL method is the capability to discover high-quality rules from low-quality data, its property of low-quality data tolerant (Dai & Ciesieski, 1994a, 2004), high efficiency in discovery, and high accuracy of the discovered rules.

Complete Chapter List

Search this Book:
Reset