| 书目名称 | Hierarchical Feature Selection for Knowledge Discovery |
| 副标题 | Application of Data |
| 编辑 | Cen Wan |
| 视频video | |
| 概述 | Discusses the state of the art in hierarchical feature selection algorithms.Reviews the applications of hierarchical feature selection algorithms to bioinformatics databases.Surveys the applications o |
| 丛书名称 | Advanced Information and Knowledge Processing |
| 图书封面 |  |
| 描述 | This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation providesthe resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical fe |
| 出版日期 | Book 2019 |
| 关键词 | Bioinformatics; Hierarchical Feature Selection; Gene Ontology; Biology of Ageing; Data Mining; Knowledge |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-97919-9 |
| isbn_ebook | 978-3-319-97919-9Series ISSN 1610-3947 Series E-ISSN 2197-8441 |
| issn_series | 1610-3947 |
| copyright | Springer Nature Switzerland AG 2019 |
发表于 2025-3-21 17:16:45
