This book provides a general overview of multiple instance learning
(MIL), defining the framework and covering the central paradigms.
The authors discuss the most important algorithms for MIL such as
classification, regression and clustering. With a focus on
classification, a taxonomy is set and the most relevant proposals
are specified. Efficient algorithms are developed to discover
relevant information when working with uncertainty. Key
representative applications are included. This book carries out a
study of the key related fields of distance metrics and alternative
hypothesis. Chapters examine new and developing aspects of MIL such
as data reduction for multi-instance problems and imbalanced MIL
data. Class imbalance for multi-instance problems is defined at the
bag level, a type of representation that utilizes ambiguity due to
the fact that bag labels are available, but the labels of the
individual instances are not defined. Additionally, multiple
instance multiple label learning is explored. This learning
framework introduces flexibility and ambiguity in the object
representation providing a natural formulation for representing
complicated objects. Thus, an object is represented by a bag of
instances and is allowed to have associated multiple class labels
simultaneously. This book is suitable for developers and engineers
working to apply MIL techniques to solve a variety of real-world
problems. It is also useful for researchers or students seeking a
thorough overview of MIL literature, methods, and tools.
General
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