Network based rule representation for knowledge discovery and predictive modelling

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Abstract

Due to the vast and rapid increase in data, data mining has been an increasingly important tool for the purpose of knowledge discovery to prevent the presence of rich data but poor knowledge. In this context, machine learning can be seen as a powerful approach to achieve intelligent data mining. In practice, machine learning is also an intelligent approach for predictive modelling. A special type of machine learning methods, which are known as rule based methods such as decision trees, can be used to build a rule based system as a special type of expert systems for both knowledge discovery and predictive modelling. A rule based system may be represented through different structures. The techniques for representing rules are known as rule representation, which is significant for knowledge discovery in relation to the interpretability of the model, as well as for predictive modelling with regard to efficiency in predicting unseen instances. This paper justifies the significance of rule representation. Some networked topologies for rule representation are introduced against existing techniques. The network topologies are validated using complexity analysis in order to show their advantages comparing with the existing techniques in terms of model interpretability and computational efficiency.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Place of PublicationIstanbul
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Print)978-1-4673-7428-6
DOIs
Publication statusPublished - Aug 2015

Keywords

  • data mining
  • machine learning
  • knowledge discovery
  • predictive modelling
  • knowledge representation
  • if-then rules
  • rule based classification

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