株式会社極東書店トップ ＞ 商品一覧 ＞ Feature Selection for High-Dimensional Data. 1st ed. 2015
商品詳細
Feature Selection for High-Dimensional Data. 1st ed. 2015
・ISBN 978-3-319-21857-1 hard EUR 49.99
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| 著者・編者 | Bolon-Canedo, Veronica / Sanchez-Marono, Noelia / Alonso-Betanzos, Amparo, |
|---|---|
| シリーズ | (Artificial Intelligence: Foundations, Theory, and Algorithms) |
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2015 |
| ページ数 | 147 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A05-43322＞ |
解説
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.