Unified framework for construction of rule based classification systems

Han Liu, Alexander Gegov, Frederic Stahl

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

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Abstract

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfitting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances. This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suitable structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations: rule generation, rule simplification and rule representation particularly on Big Data. The authors also review some existing methods and techniques used for each of the three operations and highlight the limitations of them as well as introduce some novel methods and techniques developed in their more recent research. The novel methods and techniques are also discussed in comparison to those existing ones reviewed earlier with respects to effective and efficient processing of Big Data.
Original languageEnglish
Title of host publicationInformation granularity, big data, and computational intelligence
EditorsWitold Pedrycz, Shyi-Ming Chen
Place of PublicationSwitzerland
PublisherSpringer
Pages209-230
Number of pages22
Volume8
ISBN (Electronic)9783319082547
ISBN (Print)9783319082530
DOIs
Publication statusPublished - 2015

Publication series

NameStudies in Big Data
PublisherSpringer
Volume8
ISSN (Print)2197-6503

Keywords

  • Data mining
  • Machine learning
  • Rule based systems
  • Rule based classification
  • Information granularity
  • Big data
  • Computational intelligence

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