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
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Image Database Retrieval with Multiple-Instance Learning Techniques
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Improve Multi-Instance Neural Networks through Feature Selection
Neural Processing Letters
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
An EM based multiple instance learning method for image classification
Expert Systems with Applications: An International Journal
Bayesian multiple instance learning: automatic feature selection and inductive transfer
Proceedings of the 25th international conference on Machine learning
Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
M3IC: maximum margin multiple instance clustering
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning
Information Sciences: an International Journal
Class-dependent dissimilarity measures for multiple instance learning
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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In this article, we describe a feature selection algorithm which can automatically find relevant features for multiple instance learning Multiple instance learning is considered an extension of traditional supervised learning where each example is made up of several instances and there is no specific information about particular instance labels In this scenario, traditional supervised learning can not be applied directly and it is necessary to design new techniques Our approach is based on principles of the well-known Relief-F algorithm which is extended to select features in this new learning paradigm by modifying the distance, the difference function and computation of the weight of the features Four different variants of this algorithm are proposed to evaluate their performance in this new learning framework Experiment results using a representative number of different algorithms show that predictive accuracy improves significantly when a multiple instance learning classifier is learnt on the reduced data set.