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Demand-Driven Associative Classification

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-0-85729-525-5

ISBN: 978-0-85729-524-8

Publisher: Springer London - Springer Journals

May 18, 2011

1 - 10 of 10 Chapters

Available

Introduction

Veloso, Adriano; Meira, Wagner

Pages: 3 –8

[Learning is a fundamental ability of many living organisms. It leads to the development of new skills, values, understanding, and preferences. Improved learning learning capabilities catalyze the evolution and may distinguish entire species with respect to the activities they are able to...
Available

The Classification Problem

Veloso, Adriano; Meira, Wagner

Pages: 9 –18

[We present the classification problem, starting with definitions and notations that are necessary to ground posterior discussions. Then, we discuss the Probably Approximately Correct learning framework, and some function approximation strategies.]
Available

Associative Classification

Veloso, Adriano; Meira, Wagner

Pages: 21 –37

[The hypothesis space, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt}...
Available

Demand-Driven Associative Classification

Veloso, Adriano; Meira, Wagner

Pages: 39 –49

[The ultimate goal of classification algorithms is to achieve the best possible classification performance for the problem at hand. Most often, classification performance is obtained by assessing some accuracy criterion using the test set, \documentclass[12pt]{minimal}...
Available

Multi-Label Associative Classification

Veloso, Adriano; Meira, Wagner

Pages: 53 –59

[A typical assumption in classification is that outputs are mutually exclusive, so that an input can be mapped to only one output (i.e., single-label classification ). However, due to ambiguity or multiplicity, it is quite natural that many applications violate this assumption, allowing...
Available

Competence–Conscious Associative Classification

Veloso, Adriano; Meira, Wagner

Pages: 61 –73

[The classification performance of an associative classification algorithm is strongly dependent on the statistic measure or metric that is used to quantify the strength of the association between features and classes (i.e., confidence, correlation, etc.). Previous studies have shown that...
Available

Calibrated Associative Classification

Veloso, Adriano; Meira, Wagner

Pages: 75 –86

[Given an input \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt}...
Available

Self-Training Associative Classification

Veloso, Adriano; Meira, Wagner

Pages: 87 –95

[The acquisition of training examples usually requires skilled human annotators to manually label the relationship between inputs and outputs . Due to various reasons, annotators may face inputs that are hard to label (Chapelle et al. Semi-supervised learning. MIT Press, Cambridge, 2006). The...
Available

Ordinal Regression and Ranking

Veloso, Adriano; Meira, Wagner

Pages: 97 –104

[Accurate ordering or ranking over instances is of paramount importance for several applications (Faria et al. Learning to rank for content-based image retrieval. In: Proceedings of the Multimedia Information Retrieval Conference, pp. 285–294, 2010; Veloso et al. Learning to rank at query-time...
Available

Conclusions

Veloso, Adriano; Meira, Wagner

Pages: 107 –110

[In this chapter we summarize the research contributions of this work and point out limitations and problems that remained open.]

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