| 書目名稱 | Feature Selection for High-Dimensional Data |
| 編輯 | Verónica Bolón-Canedo,Noelia Sánchez-Maro?o,Amparo |
| 視頻video | http://file.papertrans.cn/342/341566/341566.mp4 |
| 概述 | Explains how to choose an optimal subset of features according to a certain criterion.Coherent, comprehensive approach to feature subset selection in the scope of classification problems.Authors expla |
| 叢書名稱 | Artificial Intelligence: Foundations, Theory, and Algorithms |
| 圖書封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2015 |
| 關(guān)鍵詞 | Big Data; Big Dimensionality; Data Preprocessing; Data Reduction; Dimensionality Reduction; Feature Selec |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-21858-8 |
| isbn_softcover | 978-3-319-36643-2 |
| isbn_ebook | 978-3-319-21858-8Series ISSN 2365-3051 Series E-ISSN 2365-306X |
| issn_series | 2365-3051 |
| copyright | Springer International Publishing Switzerland 2015 |
|Archiver|手機版|小黑屋|
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