Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
A Note on Learning from Multiple-Instance Examples
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Approximating hyper-rectangles: learning and pseudorandom sets
Journal of Computer and System Sciences - Fourteenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
Agnostic learning of geometric patterns
Journal of Computer and System Sciences
FANNC: a fast adaptive neural network classifier
Knowledge and Information Systems
C-Net: a method for generating non-deterministic and dynamic multivariate decision trees
Knowledge and Information Systems
Data-Driven Constructive Induction
IEEE Intelligent Systems
Multiple-Instance Learning of Real-Valued Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
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
Multiple-Instance Learning of Real-Valued Geometric Patterns
Annals of Mathematics and Artificial Intelligence
Learning from ambiguity
Filtering Multi-Instance Problems to Reduce Dimensionality in Relational Learning
Journal of Intelligent Information Systems
A Novel Bag Generator for Image Database Retrieval With Multi-Instance Learning Techniques
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
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
An Extended Kernel for Generalized Multiple-Instance Learning
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Accelerating EM clustering to find high-quality solutions
Knowledge and Information Systems
Multi-Instance Learning Based Web Mining
Applied Intelligence
A binary neural k-nearest neighbour technique
Knowledge and Information Systems
Improving Promoter Prediction Using Multiple Instance Learning
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Revisiting Multiple-Instance Learning Via Embedded Instance Selection
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
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
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio
DS '09 Proceedings of the 12th International Conference on Discovery Science
Multiple-instance image database retrieval by spatial similarity based on Interval Neighbor Group
Proceedings of the ACM International Conference on Image and Video Retrieval
Grammar guided genetic programming for multiple instance learning: an experimental study
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The Knowledge Engineering Review
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
LSA based multi-instance learning algorithm for image retrieval
Signal Processing
Predicting MHC-II Binding Affinity Using Multiple Instance Regression
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multi-instance multi-label learning
Artificial Intelligence
Multiple instance learning for classifying students in learning management systems
Expert Systems with Applications: An International Journal
Latent topic based multi-instance learning method for localized content-based image retrieval
Computers & Mathematics with Applications
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning
Information Sciences: an International Journal
Multiple instance classification: Review, taxonomy and comparative study
Artificial Intelligence
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In multi-instance learning, the training set is composed of labeled bags each consists of many unlabeled instances, that is, an object is represented by a set of feature vectors instead of only one feature vector. Most current multi-instance learning algorithms work through adapting single-instance learning algorithms to the multi-instance representation, while this paper proposes a new solution which goes at an opposite way, that is, adapting the multi-instance representation to single-instance learning algorithms. In detail, the instances of all the bags are collected together and clustered into d groups first. Each bag is then re-represented by d binary features, where the value of the ith feature is set to one if the concerned bag has instances falling into the ith group and zero otherwise. Thus, each bag is represented by one feature vector so that single-instance classifiers can be used to distinguish different classes of bags. Through repeating the above process with different values of d, many classifiers can be generated and then they can be combined into an ensemble for prediction. Experiments show that the proposed method works well on standard as well as generalized multi-instance problems.