Feature Selection in Data Mining
Approaches Based on Information Theory
(Sprache: Englisch)
In many predictive modeling tasks, one has a fixed set of observations fromwhich a vast, or even infinite, set of potentially predictive features can becomputed. Of these features, often only a small number are expected to beuseful in a predictive model....
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Produktinformationen zu „Feature Selection in Data Mining “
In many predictive modeling tasks, one has a fixed set of observations from
which a vast, or even infinite, set of potentially predictive features can be
computed. Of these features, often only a small number are expected to be
useful in a predictive model. Models which use the entire set of features will
almost certainly overfit on future data sets.
The book presents streamwise feature selection which interleaves the process
of generating new features with that of feature testing. Streamwise feature
selection scales well to large feature sets. The book also describes how to use
streamwise feature seleciton in multivariate regressions.
It includes a review of traditional feature selecitions in a general framework
based on information theory, and compares these methods with streamwise
feature selection on various real and synthetic data sets. This book is
intended to be used by researchers in machine learning, data mining, and
knowledge discovery.
Klappentext zu „Feature Selection in Data Mining “
In many predictive modeling tasks, one has a fixed set of observations from which a vast, or even infinite, set of potentially predictive features can be computed. Of these features, often only a small number are expected to be useful in a predictive model. Models which use the entire set of features will almost certainly overfit on future data sets.The book presents streamwise feature selection which interleaves the process of generating new features with that of feature testing. Streamwise feature selection scales well to large feature sets. The book also describes how to use streamwise feature seleciton in multivariate regressions.It includes a review of traditional feature selecitions in a general framework based on information theory, and compares these methods with streamwise feature selection on various real and synthetic data sets. This book is intended to be used by researchers in machine learning, data mining, and knowledge discovery.
Autoren-Porträt von Jing Zhou
Jing Zhouis an Applied Science Researcher in Microsoft,solving research problems that impact businessperformance and building advanced prototypes.He received a Ph.D. from the University ofPennsylvania.
Bibliographische Angaben
- Autor: Jing Zhou
- 2007, 104 Seiten, Maße: 17 x 24 cm, Kartoniert (TB), Englisch
- Verlag: VDM Verlag Dr. Müller
- ISBN-10: 3836427117
- ISBN-13: 9783836427111
Sprache:
Englisch
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