Feature Extraction, Construction and Selection: A Data Mining Perspective / Edition 1
by Huan LiuThe 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… See more details below
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.
Product Details
- ISBN-13:
- 9780792381969
- Publisher:
- Springer US
- Publication date:
- 07/01/1998
- Series:
- Springer International Series in Engineering and Computer Science, #453
- Edition description:
- 1998
- Pages:
- 410
- Product dimensions:
- 9.21(w) x 6.14(h) x 1.00(d)
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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