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
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
A Framework for Learning Rules from Multiple Instance Data
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth 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
SVM-based generalized multiple-instance learning via approximate box counting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multi-Instance Learning Based Web Mining
Applied Intelligence
Adapting RBF Neural Networks to Multi-Instance Learning
Neural Processing Letters
Ensembles of multi-instance neural networks
Intelligent information processing II
Incorporating multiple SVMs for automatic image annotation
Pattern Recognition
Mining multiple comprehensible classification rules using genetic programming
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
JCLEC: a Java framework for evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (pp 315-357) "Ordered structures in many-valued logic"
Multiple Instance Learning with Multiple Objective Genetic Programming for Web Mining
Applied Soft Computing
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This paper introduces the use of multi-objective evolutionary algorithms in multiple instance learning. In order to achieve this purpose, a multi-objective grammar-guided genetic programming algorithm (MOG3P-MI) has been designed. This algorithm has been evaluated and compared to other existing multiple instance learning algorithms. Research on the performance of our algorithm is carried out on two well-known drug activity prediction problems, Musk and Mutagenesis, both problems being considered typical benchmarks in multiple instance problems. Computational experiments indicate that the application of the MOG3P-MI algorithm improves accuracy and decreases computational cost with respect to other techniques.