Feature Selection for High-Dimensional Data

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This book offers a coherent and comprehensive approach to feature subset selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection for high-dimensional data. The authors first focus on the analysis and synthesis of feature selection algorithms, presenting a comprehensive review of basic concepts and experimental results of the most well-known algorithms. They then address different real scenarios with high-dimensional data, showing the use of feature selection algorithms in different contexts with different requirements and information: microarray data, intrusion detection, tear film lipid layer classification and cost-based features. The book then delves into the scenario of big dimension, paying attention to important problems under high-dimensional spaces, such as scalability, distributed processing and real-time processing, scenarios that open up new and interesting challenges for researchers. The book is useful for practitioners, researchers and graduate students in the areas of machine learning and data mining.

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Ketersediaan

Informasi Detail

Judul Seri

Artificial Intelligence: Foundations, Theory, and Algorithms

No. Panggil

006.3 BOL f

Penerbit

Cham, Switzerland : Springer International Publishing., 2015

Deskripsi Fisik

XV, 147 hlm. : ilus. ; 25 cm

Bahasa

English

ISBN/ISSN

978-3-319-21857-1

Klasifikasi

006.3

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

-

Subjek

Artificial Intelligence
Data Mining
Data structures (Computer science)

Info Detail Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

Lampiran Berkas

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