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Large Scale Hierarchical Classification: State of the Art (SpringerBriefs in Computer Science),Used
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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 stateoftheart 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 hierarchy2. Incorporating relationships during model learning leads to optimization issues3. Feature selection4. Scalability due to large number of examples, features and classes5. Hierarchical inconsistencies6. Error propagation due to multiple decisions involved in making predictions for topdown methodsThe brief also demonstrates how multiple hierarchies can be leveraged forimproving the HC performance using different MultiTask Learning (MTL) frameworks.The purpose of this book is twofold: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, multitask learning and feature selection for HC. Its results are highly competitive with the stateoftheart approaches in the literature.
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