Feature Extraction, Construction and Selection: A Data Mining Perspective

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Overview

The book can be used by researchers and graduate students in machine learning, data mining, and knowledge discovery, who wish to understand techniques of feature extraction, construction and selection for data pre-processing and to solve large size, real-world problems. The book can also serve as a reference book for those who are conducting research about feature extraction, construction and selection, and are ready to meet the exciting challenges ahead of us.

Editorial Reviews

Booknews

Two dozen contributions disseminate among the data mining community a variety of methods for extracting, constructing, and selecting features from a large database. The first part includes studies of background, foundation, and general approaches. The other four parts can each stand alone; they describe selecting subsets, extracting features, constructing features, and combined approaches. Among the specific topics are the wrapper approach, selecting features by the vertical compactness of data, lexical contextual relations for the unsupervised discovery of texts features, constructing different types of new features for decision-tree learning, transforming features by decomposing functions, and feature selection based on an interactive genetic algorithm and its application to marketing data analysis. Annotation c. by Book News, Inc., Portland, Or.

Product Details

ISBN-13: 9780792381969
Publisher: Springer US
Publication date: 7/1/1998
Series: Springer International Series in Engineering and Computer Science , #453
Edition description: 1998
Edition number: 1
Pages: 410
Product dimensions: 9.21 (w) x 6.14 (h) x 1.00 (d)

	Preface
	Acknowledgments
	Contributing Authors
1	Less is More	3
2	Feature Weighting for Lazy Learning Algorithms	13
3	The Wrapper Approach	33
4	Data-driven Constructive Induction: Methodology and Applications	51
5	Selecting Features by Vertical Compactness of Data	71
6	Relevance Approach to Feature Subset Selection	85
7	Novel Methods for Feature Subset Selection with Respect to Problem Knowledge	101
8	Feature Subset Selection Using A Genetic Algorithm	117
9	A Relevancy Filter for Constructive Induction	137
10	Lexical Contextual Relations for the Unsupervised Discovery of Texts Features	157
11	Integrated Feature Extraction Using Adaptive Wavelets	175
12	Feature Extraction via Neural Networks	191
13	Using Lattice-based Framework as a Tool for Feature Extraction	205
14	Constructive Function Approximation	219
15	A Comparison of Constructing Different Types of New Feature for Decision Tree Learning	239
16	Constructive Induction: Covering Attribute Spectrum	257
17	Feature Construction Using Fragmentary Knowledge	273
18	Constructive Induction on Continuous Spaces	289
19	Evolutionary Feature Space Transformation	307
20	Feature Transformation by Function Decomposition	325
21	Constructive Induction of Cartesian Product Attributes	341
22	Towards Automatic Fractal Feature Extraction for Image Recognition	357
23	Feature Transformation Strategies for a Robot Learning Problem	375
24	Interactive Genetic Algorithm Based Feature Selection and Its Application to Marketing Data Analysis	393
	Index	407

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Feature Extraction, Construction and Selection: A Data Mining Perspective / Edition 1