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

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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.
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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.
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Product Details

Table of Contents

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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