| 书目名称 | Recent Advances in Ensembles for Feature Selection |
| 编辑 | Verónica Bolón-Canedo,Amparo Alonso-Betanzos |
| 视频video | |
| 概述 | Offers a comprehensive overview of ensemble learning in the field of feature selection (FS).Provides the user with the background and tools needed to develop new ensemble methods for feature selection |
| 丛书名称 | Intelligent Systems Reference Library |
| 图书封面 |  |
| 描述 | .This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance. ..With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative...The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges thatresearchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining. . |
| 出版日期 | Book 2018 |
| 关键词 | Ensemble Learning; Information Fusion; Machine Learning; Pattern Recognition; Data Reduction; Dimensional |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-90080-3 |
| isbn_softcover | 978-3-030-07929-1 |
| isbn_ebook | 978-3-319-90080-3Series ISSN 1868-4394 Series E-ISSN 1868-4408 |
| issn_series | 1868-4394 |
| copyright | Springer International Publishing AG, part of Springer Nature 2018 |
发表于 2025-3-21 18:37:21
