Feature Extraction, Construction and Selection: A Data Mining Perspective (The Springer International Series in Engineering and Computer Science, nr. 453)

Editat de Huan Liu, Hiroshi Motoda
Notă GoodReads:
en Limba Engleză Hardback – 31 Aug 1998
There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today's advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem.
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ISBN-13: 9780792381969
ISBN-10: 0792381963
Pagini: 410
Ilustrații: XXIV, 410 p.
Dimensiuni: 155 x 235 x 30 mm
Greutate: 0.74 kg
Ediția: 1998
Editura: Springer Us
Colecția Springer
Seria The Springer International Series in Engineering and Computer Science

Locul publicării: New York, NY, United States

Public țintă



Preface. Part I: Background and Foundation. 1. Less is More; Huan Liu, H. Motoda. 2. Feature Weighting for Lazy Learning Algorithms; D.W. Aha. 3. The Wrapper Approach; R. Kohavi, G.H. John. 4. Data-driven Constructive Induction: Methodology and Applications; E. Bloedorn, R.S. Michalski. Part II: Subset Selection. 5. Selecting Features by Vertical Compactness of Data; Ke Wang, S. Sundaresh. 6. Relevance Approach to Feature Subset Selection; Hui Wang, et al. 7. Novel Methods for Feature Subset Selection with Respect to Problem Knowledge; P. Pudil, J. Novovicová. 8. Feature Subset Selection Using a Genetic Algorithm; Jihoon Yang, V. Honavar. 9. A Relevancy Filter for Constructive Induction; N. Lavrac, et al. Part III: Feature Extraction. 10. Lexical Contextual Relations for the Unsupervised Discovery of Texts Features; P. Perrin, F. Petry. 11. Integrated Feature Extraction Using Adaptive Wavelets; Y. Mallet, et al. 12. Feature Extraction via Neural Networks; R. Setiono, Huan Liu. 13. Using Lattice-based Framework as a Tool for Feature Extraction; E. Mephu Nguifo, P. Njiwoua. 14. Constructive Function Approximation; P.E. Utgoff, D. Precup. Part IV: Feature Construction. 15. A Comparison of Constructing Different Types of New Feature for Decision Tree Learning; Zijian Zheng. 16. Constructive Induction: Covering Attribute Spectrum; Yuh-Jyh Hu. 17. Feature Construction Using Fragmentary Knowledge; S. Donoho, L. Rendell.18. Constructive Induction on Continuous Spaces; J. Gama, P. Brazdil. Part V: Combined Approaches. 19. Evolutionary Feature Space Transformation; H. Vafaie, K. De Jong. 20. Feature Transformation by Function Decomposition; B. Zupan, et al. 21. Constructive Induction of Cartesian Product Attributes; M.J. Pazzani. Part VI: Applications of Feature Transformation. 22. Towards Automatic Fractal Feature Extraction for Image Recognition; M. Baldoni, et al. 23. Feature Transformation Strategies for a Robot Learning Problem; L.S. Lopes, L.M. Camarinha-Matos. 24. Interactive Genetic Algorithm Based Feature Selection and Its Application to Marketing Data Analysis; T. Terano, Y. Ishino. Index.