Representation of classification rules

Han Liu; Alexander Gegov; Mihaela Cocea

doi:10.1007/978-3-319-23696-4_5

Representation 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, appropriate rule representation is necessary in order to improve model efficiency and interpretability. This chapter introduces three techniques for representation of classification rules namely, decision trees, linear lists and rule based networks. In particular, these representations are illustrated using examples in terms of searching for firing rules. These techniques are also discussed comparatively in terms of computational complexity and interpretability.

Original language	English
Title of host publication	Rule Based Systems for Big Data
Subtitle of host publication	A Machine Learning Approach
Publisher	Springer
Pages	51-62
Number of pages	12
Edition	1st
ISBN (Electronic)	9783319236964
ISBN (Print)	9783319236957, 9783319370279
DOIs	https://doi.org/10.1007/978-3-319-23696-4_5 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

Access to Document

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

Cite this

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