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Demand-Driven Associative Classification

  • Adriano Veloso
  • Wagner Meira Jr.

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Introduction and Preliminaries

    1. Front Matter
      Pages 1-1
    2. Adriano Veloso, Wagner Meira Jr.
      Pages 3-8
    3. Adriano Veloso, Wagner Meira Jr.
      Pages 9-18
  3. Associative Classification

    1. Front Matter
      Pages 19-19
    2. Adriano Veloso, Wagner Meira Jr.
      Pages 21-37
    3. Adriano Veloso, Wagner Meira Jr.
      Pages 39-49
  4. Extensions to Associative Classification

    1. Front Matter
      Pages 51-51
    2. Adriano Veloso, Wagner Meira Jr.
      Pages 53-59
    3. Adriano Veloso, Wagner Meira Jr.
      Pages 61-73
    4. Adriano Veloso, Wagner Meira Jr.
      Pages 75-86
    5. Adriano Veloso, Wagner Meira Jr.
      Pages 87-95
    6. Adriano Veloso, Wagner Meira Jr.
      Pages 97-104
  5. Conclusions and Future Work

    1. Front Matter
      Pages 105-105
    2. Adriano Veloso, Wagner Meira Jr.
      Pages 107-110
  6. Back Matter
    Pages 111-112

About this book

Introduction

The ultimate goal of machines is to help humans to solve problems.
Such problems range between two extremes: structured problems for which the solution is totally defined (and thus are easily programmed by humans), and random problems for which the solution is completely undefined (and thus cannot be programmed). Problems in the vast middle ground have solutions that cannot be well defined and are, thus, inherently hard to program. Machine Learning is the way to handle this vast middle ground, so that many tedious and difficult hand-coding tasks would be replaced by automatic learning methods. There are several machine learning tasks, and this work is focused on a major one, which is known as classification. Some classification problems are hard to solve, but we show that they can be decomposed into much simpler sub-problems. We also show that independently solving these sub-problems by taking into account their particular demands, often leads to improved classification performance.

Keywords

Associative Classification Associative Rules Calibrated Classification Learning to Rank Multi-Label Classification

Authors and affiliations

  • Adriano Veloso
    • 1
  • Wagner Meira Jr.
    • 2
  1. 1., Computer Science DepartmentUniversidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2., Computer Science DepartmentUniversidade Federal de Minas GeraisBelo HorizonteBrazil

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-85729-525-5
  • Copyright Information Adriano Veloso 2011
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-0-85729-524-8
  • Online ISBN 978-0-85729-525-5
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site