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Large scale hierarchical classificat...

Naik, Azad.

Large scale hierarchical classificationstate of the art /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Large scale hierarchical classificationby Azad Naik, Huzefa Rangwala.
Reminder of title:	state of the art /
Author:	Naik, Azad.
other author:	Rangwala, Huzefa.
Published:	Cham :Springer International Publishing :2018.
Description:	xvi, 93 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Supervised learning (Machine learning)
Online resource:	https://doi.org/10.1007/978-3-030-01620-3
ISBN:	9783030016203$q(electronic bk.)

Large scale hierarchical classificationstate of the art /
Naik, Azad.

Large scale hierarchical classificationstate of the art /[electronic resource] :by Azad Naik, Huzefa Rangwala. - Cham :Springer International Publishing :2018. - xvi, 93 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

1 Introduction -- 2 Background and Literature Review -- 3 Hierarchical Structure Inconsistencies -- 4 Large Scale Hierarchical Classification with Feature Selection -- 5 Multi-Task Learning -- 6 Conclusions and Future Research Directions.

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 for improving 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. Provide several research directions that have not yet been explored extensively to advance the research boundaries in HC. New approaches discussed in this book include detailed information corresponding to the hierarchical inconsistencies, multi-task learning and feature selection for HC. Its results are highly competitive with the state-of-the-art approaches in the literature.

ISBN: 9783030016203$q(electronic bk.)

Standard No.: 10.1007/978-3-030-01620-3doiSubjects--Topical Terms:

209220
Supervised learning (Machine learning)

LC Class. No.: Q325.75

Dewey Class. No.: 006.31

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520 $a 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 for improving 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. Provide several research directions that have not yet been explored extensively to advance the research boundaries in HC. New approaches discussed in this book include detailed information corresponding to the hierarchical inconsistencies, multi-task learning and feature selection for HC. Its results are highly competitive with the state-of-the-art approaches in the literature.
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