书目名称 | Large Scale Hierarchical Classification: State of the Art |
编辑 | Azad Naik,Huzefa Rangwala |
视频video | |
丛书名称 | SpringerBriefs in Computer Science |
图书封面 |  |
描述 | .This SpringerBrief covers the technical material related to large scale hierarchical classification (LSHC). HC is an important machine learning problem that has been researched and explored extensively in the past few years. In this book, the authors provide a comprehensive overview of various state-of-the-art existing methods and algorithms that were developed to solve the HC problem in large scale domains. Several challenges faced by LSHC is discussed in detail such as: .. 1. High imbalance between classes at different levels of the hierarchy..2. Incorporating relationships during model learning leads to optimization issues ..3. Feature selection ..4. Scalability due to large number of examples, features and classes ..5. Hierarchical inconsistencies ..6. Error propagation due to multiple decisions involved in making predictions for top-down methods.. The brief also demonstrates how multiple hierarchies can be leveraged forimproving the HC performance using different Multi-Task Learning (MTL) frameworks... The purpose of this book is two-fold:..1. Help novice researchers/beginners to get up to speed by providing a comprehensive overview of several existing techniques. ..2. Provid |
出版日期 | Book 2018 |
关键词 | hierarchical classification; hierarchical inconsistencies; data mining; artificial intelligence; large s |
版次 | 1 |
doi | https://doi.org/10.1007/978-3-030-01620-3 |
isbn_softcover | 978-3-030-01619-7 |
isbn_ebook | 978-3-030-01620-3Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
issn_series | 2191-5768 |
copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2018 |