Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Improve Multi-Instance Neural Networks through Feature Selection
Neural Processing Letters
Multi-Instance Learning Based Web Mining
Applied Intelligence
Region based image annotation through multiple-instance learning
Proceedings of the 13th annual ACM international conference on Multimedia
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Incorporating multiple SVMs for automatic image annotation
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Bayesian multiple instance learning: automatic feature selection and inductive transfer
Proceedings of the 25th international conference on Machine learning
Multi-instance genetic programming for web index recommendation
Expert Systems with Applications: An International Journal
Editorial: Hybrid learning machines
Neurocomputing
Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
A human-centered multiple instance learning framework for semantic video retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
M3IC: maximum margin multiple instance clustering
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
MILC2: a multi-layer multi-instance learning approach to video concept detection
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
G3P-MI: A genetic programming algorithm for multiple instance learning
Information Sciences: an International Journal
Multiple instance learning for classifying students in learning management systems
Expert Systems with Applications: An International Journal
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In machine learning the so-called curse of dimensionality, pertinent to many classification algorithms, denotes the drastic increase in computational complexity and classification error with data having a great number of dimensions. In this context, feature selection techniques try to reduce dimensionality finding a new more compact representation of instances selecting the most informative features and removing redundant, irrelevant, and/or noisy features. In this paper, we propose a filter-based feature selection method for working in the multiple-instance learning scenario called ReliefF-MI; it is based on the principles of the well-known ReliefF algorithm. Different extensions are designed and implemented and their performance checked in multiple instance learning. ReliefF-MI is applied as a pre-processing step that is completely independent from the multi-instance classifier learning process and therefore is more efficient and generic than wrapper approaches proposed in this area. Experimental results on five benchmark real-world data sets and 17 classification algorithms confirm the utility and efficiency of this method, both statistically and from the point of view of execution time.