Multiple Instance Learning
Foundations and Algorithms
Published on 29. June 2018
Book
Paperback/Softback
XI, 233 pages
978-3-319-83815-1 (ISBN)
Description
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.
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.
More details
Edition
Softcover reprint of the original 1st ed. 2016
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
40 farbige Abbildungen, 6 s/w Abbildungen
XI, 233 p. 46 illus., 40 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 14 mm
Weight
382 gr
ISBN-13
978-3-319-83815-1 (9783319838151)
DOI
10.1007/978-3-319-47759-6
Schweitzer Classification
Other editions
Additional editions
Francisco Herrera | Sebastián Ventura | Rafael Bello
Multiple Instance Learning
Foundations and Algorithms
Book
11/2016
Springer
€106.99
Shipment within 10-15 days
Persons
Julián Luengo received the M.S. degree in computer science and the Ph.D. from the University of Granada, Granada, Spain, in 2006 and 2011 respectively. He currently acts as an Assistant Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada, Spain. His research interests include machine learning and data mining, data preparation in knowledge discovery and data mining, missing values, noisy data, data complexity and fuzzy systems. Dr. Luengo has been given some awards and honors for his personal work or for his publications in and conferences, such as IFSA-EUSFLAT 2009 Best Student Paper Award. He belongs to the list of the Highly Cited Researchers in the area of Computer Sciences (2015- 2018) (Clarivate Analytics).
Diego Garc¿¿a-Gil received the M.Sc. degree in computer science from the University of Granada, Granada, Spain, in 2015. He is currently pursuing the Ph.D. degree with the Department ofComputer Science and Artificial Intelligence, University of Granada, Granada, Spain. His current research interests include machine learning, data mining, data preprocessing and Big Data.
Sergio Ramírez-Gallego received the M.Sc. degree in computer science from the University of Jaén, Jaén, Spain, in 2012. He obtained the Ph.D. degree with the Department of Computer Science and Artificial Intelligence, University of Granada, Spain in 2018. His current research interests include data mining, data preprocessing, big data, and cloud computing.
Salvador García received the B.S. and Ph.D. degrees in Computer Science from the University of Granada, Granada, Spain, in 2004 and 2008, respectively. He is currently an Associate Professor in the Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain. Dr. García has published more than 80 papers in international journals (more than60 in Q1), h-index 43, over 60 papers in international conference proceedings (data from Web of Science). He has organized several special sessions and workshops related to data preprocessing and evolutionary learning in conferences such as "Hybrid Intelligent Systems", "Intelligent Systems Design and Applications" and "International Joint-Conference of Neural Networks". He has been associated with the international program committees and organizing committees of several regular international conferences including IEEE CEC, ICPR, ICDM, IJCAI, etc. As edited activities, he has co-edited two special issues in international journals and he is an associate editor of "Information Fusion" (Elsevier), "Swarm and Evolutionary Computation" (Elsevier) and "AI Communications" (IOS Press) journals, and he is co-Editor in Chief of the international journal "Progress in Artificial Intelligence" (Springer). He is a co-author of the books entitled "Data Preprocessing in Data Mining" and "Learning fromImbalanced Data Sets" published by Springer. His research interests include data science, data preprocessing, Big Data, evolutionary learning, Deep Learning, metaheuristics and biometrics.
Francisco Herrera (SM'15) received his M.Sc. in Mathematics in 1988 and Ph.D. in Mathematics in 1991, both from the University of Granada, Spain. He is currently a Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada and Director of DaSCI Institute (Andalusian Research Institute in Data Science and Computational Intelligence). He has been the supervisor of 44 Ph.D. students. He has published more than 400 journal papers, receiving more than 66000 citations (Scholar Google, H-index 132). He is co-author of the books "Genetic Fuzzy Systems" (World Scientific, 2001) and "Data Preprocessing in Data Mining" (Springer, 2015), "The 2-tuple Linguistic Model. Computing with Words in Decision Making" (Springer, 2015), "Multilabel Classification. Problem analysis, m
Content
Introduction.- Multiple Instance Learning.- Multi-Instance Classification.- Instance-Based Classification Methods.- Bag-Based Classification Methods.- Multi-Instance Regression.- Unsupervised Multiple Instance Learning.- Data Reduction.- Imbalance Multi-Instance Data.- Multiple Instance Multiple Label Learning.