Generation of classification rules

Han Liu; Alexander Gegov; Mihaela Cocea

doi:10.1007/978-3-319-23696-4_3

Generation of classification rules

Han Liu, Alexander Gegov, Mihaela Cocea

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

Abstract

As mentioned in Chap. 1, rule generation can be done through the use of the two approaches: divide and conquer and separate and conquer. This chapter describes the two approaches of rule generation. In particular, the existing rule learning algorithms, namely ID3, Prism and Information Entropy Based Rule Generation (IEBRG), are illustrated in detail. These algorithms are also discussed comparatively with respects to their advantages and disadvantages.

Original language	English
Title of host publication	Rule Based Systems for Big Data
Subtitle of host publication	A Machine Learning Approach
Publisher	Springer
Pages	29-42
Number of pages	14
Edition	1st
ISBN (Electronic)	9783319236964
ISBN (Print)	9783319236957, 9783319370279
DOIs	https://doi.org/10.1007/978-3-319-23696-4_3 https://doi.org/10.1007/978-3-319-23696-4
Publication status	Published - 17 Sept 2015

Publication series

Name	Studies in Big Data
Volume	13
ISSN (Print)	2197-6503
ISSN (Electronic)	2197-6511

Keywords

average entropy
conditional entropy
decision tree learning
target class
training subset

Access to Document

https://link.springer.com/book/10.1007/978-3-319-23696-4

Cite this

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