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 language | English |
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Title of host publication | Advances in Computational Intelligence Systems |
Subtitle of host publication | Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK |
Editors | Plamen Angelov, Alexander Gegov, Chrisina Jayne, Qiang Shen |
Publisher | Springer |
Pages | 125-143 |
Number of pages | 19 |
ISBN (Electronic) | 978-3-319-46562-3 |
ISBN (Print) | 978-3-319-46561-6 |
DOIs | |
Publication status | Published - 7 Sept 2016 |
Event | 16th Annual UK Workshop on Computational Intelligence - Lancaster University, Lancaster, United Kingdom Duration: 7 Sept 2016 → 9 Sept 2016 http://wp.lancs.ac.uk/ukci2016/ http://wp.lancs.ac.uk/ukci2016/ |
Publication series
Name | Advances in Intelligent Systems and Computing |
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Publisher | Springer International Publishing |
Volume | 513 |
ISSN (Print) | 2194-5357 |
Conference
Conference | 16th Annual UK Workshop on Computational Intelligence |
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Abbreviated title | UKCI 2016 |
Country/Territory | United Kingdom |
City | Lancaster |
Period | 7/09/16 → 9/09/16 |
Internet address |
Keywords
- machine learning
- rule based models
- model complexity
- complex-ity control
- rule based classification