Complexity control in rule based models for classification in machine learning context

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Abstract

A rule based model is a special type of computational model, which can be built by using expert knowledge or learning from real data. In this context, rule based modelling approaches can be divided into two categories: expert based approaches and data based approaches. Due to the vast and rapid increase in data, the latter approach has become increasingly popular for building rule based models. In machine learning context, rule based models can be evaluated in three main dimensions, namely accuracy, efficiency and interpretability. All these dimensions are usually affected by the key characteristic of a rule based model which is typically referred to as model complexity. This paper focuses on theoretical and empirical analysis of complexity of rule based models, especially for classification tasks. In particular, the significance of model complexity is argued and a list of impact factors against the complexity are identified. This paper also proposes several techniques for effective control of model complexity, and experimental studies are re-ported for presentation and discussion of results in order to analyse critically and comparatively the extent to which the proposed techniques are effective in control of model complexity.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationContributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK
EditorsPlamen Angelov, Alexander Gegov, Chrisina Jayne, Qiang Shen
PublisherSpringer
Pages125-143
Number of pages19
ISBN (Electronic)978-3-319-46562-3
ISBN (Print)978-3-319-46561-6
DOIs
Publication statusPublished - 7 Sep 2016
Event16th Annual UK Workshop on Computational Intelligence - Lancaster University, Lancaster, United Kingdom
Duration: 7 Sep 20169 Sep 2016
http://wp.lancs.ac.uk/ukci2016/
http://wp.lancs.ac.uk/ukci2016/

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer International Publishing
Volume513
ISSN (Print)2194-5357

Conference

Conference16th Annual UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2016
CountryUnited Kingdom
CityLancaster
Period7/09/169/09/16
Internet address

Keywords

  • machine learning
  • rule based models
  • model complexity
  • complex-ity control
  • rule based classification

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