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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