Complexity Control in Rule Based Models for Classification in Machine Learning Context View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Han Liu , Alexander Gegov , Mihaela Cocea

ABSTRACT

A rule based model is a special type of computational models, 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 reported for presentation and discussion of results in order to analyze critically and comparatively the extent to which the proposed techniques are effective in control of model complexity. More... »

PAGES

125-143

References to SciGraph publications

  • 1993-02. Overfitting avoidance as bias in MACHINE LEARNING
  • 2016. Induction of Modular Classification Rules by Information Entropy Based Rule Generation in INNOVATIVE ISSUES IN INTELLIGENT SYSTEMS
  • 2016. Rule Based Systems for Big Data, A Machine Learning Approach in NONE
  • 2016. Interpretability of Computational Models for Sentiment Analysis in SENTIMENT ANALYSIS AND ONTOLOGY ENGINEERING
  • 2015. Unified Framework for Construction of Rule Based Classification Systems in INFORMATION GRANULARITY, BIG DATA, AND COMPUTATIONAL INTELLIGENCE
  • 1999-02. Separate-and-Conquer Rule Learning in ARTIFICIAL INTELLIGENCE REVIEW
  • 2014. Categorization and Construction of Rule Based Systems in ENGINEERING APPLICATIONS OF NEURAL NETWORKS
  • Book

    TITLE

    Advances in Computational Intelligence Systems

    ISBN

    978-3-319-46561-6
    978-3-319-46562-3

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-46562-3_9

    DOI

    http://dx.doi.org/10.1007/978-3-319-46562-3_9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1016714323


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