A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
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
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
ICML '02 Proceedings of the Nineteenth 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
Image Database Retrieval with Multiple-Instance Learning Techniques
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
An introduction to variable and feature selection
The Journal of Machine Learning Research
Improve Multi-Instance Neural Networks through Feature Selection
Neural Processing Letters
SVM-based generalized multiple-instance learning via approximate box counting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
ICML '05 Proceedings of the 22nd international conference on Machine learning
Supervised versus multiple instance learning: an empirical comparison
ICML '05 Proceedings of the 22nd international conference on Machine learning
Adapting RBF Neural Networks to Multi-Instance Learning
Neural Processing Letters
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Ensembles of multi-instance neural networks
Intelligent information processing II
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)
Incorporating multiple SVMs for automatic image annotation
Pattern Recognition
Solving multi-instance problems with classifier ensemble based on constructive clustering
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
On the relation between multi-instance learning and semi-supervised learning
Proceedings of the 24th international conference on Machine learning
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
A Multi-Instance Learning Algorithm Based on Normalized Radial Basis Function Network
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Multi-instance genetic programming for web index recommendation
Expert Systems with Applications: An International Journal
Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
An optimization of ReliefF for classification in large datasets
Data & Knowledge Engineering
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
Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Sciences: an International Journal
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
Multiple Instance Learning with Multiple Objective Genetic Programming for Web Mining
Applied Soft Computing
G3P-MI: A genetic programming algorithm for multiple instance learning
Information Sciences: an International Journal
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
Reducing dimensionality in multiple instance learning with a filter method
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Effective automatic image annotation via integrated discriminative and generative models
Information Sciences: an International Journal
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Feature selection techniques have been successfully applied in many applications for making supervised learning more effective and efficient. These techniques have been widely used and studied in traditional supervised learning settings, where each instance is expected to have a label. In multiple instance learning (MIL) each example or bag consists of a variable set of instances, and the label is known for the bag as a whole, but not for the individual instances it consists of. Therefore utilizing these labels for feature selection in MIL becomes less straightforward. In this paper we study a new feature subset selection method for MIL called HyDR-MI (hybrid dimensionality reduction method for multiple instance learning). The hybrid consists of the filter component based on an extension of the ReliefF algorithm developed for working with MIL and the wrapper component based on a genetic algorithm that optimizes the search for the best feature subset from a reduced set of features, output by the filter component. We conducted an extensive experimental evaluation of our method on five benchmark datasets and 17 classification algorithms for MIL. The results of our study show the potential of the proposed hybrid with respect to the desirable effect it produces: a significant improvement of the predictive performance of many MIL classification techniques as compared to the effect of filter-based feature selection. This is achieved due to the possibility to decide how many of the top ranked features are useful for each particular algorithm and the possibility to discard redundant attributes.