Synopses & Reviews
There is a broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data pre-processing 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-the-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 research into feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of an 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. 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 work for those who are conducting research into feature extraction, construction and selection, and are ready to meet the exciting challenges ahead of us.
Book News Annotation:
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 (booknews@booknews.com)
Table of Contents
Less is more / Huan Liu and Hiroshi Motoda — Feature weighting for lazy learning algorithms / David W. Aha — The wrapper approach / Ron Kohavi and George H. John — Data-driven constructive induction: methodology and applications / Eric Bloedorn, and Ryszard S. Michalski — Selecting features by vertical compactness of data / Ke Wang and Suman Sundaresh — Relevance approach to feature subset selection / Hui Wang, David Bell, and Fionn Murtagh — Novel methods for feature subset selection with respect to problem knowledge / Pavel Pudil and Jana Novovicova — Feature subset selection using a genetic algorithm / Jihoon Yang and Vasant Honavar — A relevancy filter for constructive induction / Nada Lavrac, Dragan Gamberger, and Peter Turney — Lexical contextual relations for the unsupervised discovery of texts features / Patrick Perrin and Fred Petry — Integrated feature extraction using adaptive wavelets / Yvette Mallet, Olivier de Vel, and Danny Coomans — Feature extraction via neural networks / Rudy Setiono and Huan Liu — Using lattice-based framework as a tool for feature extraction / E. Mephu Nguifo, P. Njiwoua — Constructive function approximation / Paul E. Utgoff and Doina Precup — A comparison of constructing different types of new feature for decision tree learning / Zijian Zheng — Constructive induction: covering attribute spectrum / Yuh-Jyh Hu — Feature construction using fragmentary knowledge / Steve Donoho and Larry Rendell — Constructive induction on continuous spaces / Joao Gama and Pavel Brazdil — Evolutionary feature space transformation / Haleh Vafaie and Kenneth De Jong — Feature transformation by function decomposition / Blaz Zupan ... et al. — Constructive induction of Cartesian product attributes / Michael J. Pazzani — Towards automatic fractal feature extraction for image recognition / Matteo Baldoni ... et al. — Feature transformation strategies for a robot learning problem / Luis Seabra Lopes, Luis M. Camarinha-Matos — Interactive genetic algorithm based feature selection and its application to marketing data analysis / Takao Terano and Yoko Ishino.
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