Demand-Driven Associative Classification / by Adriano Veloso, Wagner Meira Jr.

Format:

Book

Author/Creator:

Veloso, Adriano, author.

Meira Jr., Wagner, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

SpringerBriefs in computer science 2191-5768

SpringerBriefs in Computer Science, 2191-5768

Language:

English

Subjects (All):

Data mining.

Mathematical statistics.

Data Mining and Knowledge Discovery.

Probability and Statistics in Computer Science.

Local Subjects:

Data Mining and Knowledge Discovery.

Probability and Statistics in Computer Science.

Physical Description:

1 online resource (XIII, 112 pages) : 27 illustrations.

Edition:

First edition 2011.

Contained In:

Springer eBooks

Place of Publication:

London : Springer London : Imprint: Springer, 2011.

System Details:

text file PDF

Summary:

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.

Contents:

Introduction and Preliminaries

Introduction

The Classification Problem

Associative Classification

Demand-Driven Associative Classification

Extensions to Associative Classification

Multi-Label Associative Classification

Competence-Conscious Associative Classification

Calibrated Associative Classification

Self-Training Associative Classification

Ordinal Regression and Ranking

Conclusions and FutureWork.

Other Format:

Printed edition:

ISBN:

978-0-85729-525-5

9780857295255

Access Restriction:

Restricted for use by site license.