Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Top-down induction of first-order logical decision trees
Artificial Intelligence
Machine Learning
Top-Down Induction of Relational Model Trees in Multi-instance Learning
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Multi-instance learning by treating instances as non-I.I.D. samples
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
On the Use of Clustering in Possibilistic Decision Tree Induction
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A phase transition-based perspective on multiple instance kernels
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
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
MIForests: multiple-instance learning with randomized trees
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Multi-instance multi-label learning
Artificial Intelligence
Beyond trees: adopting MITI to learn rules and ensemble classifiers for multi-instance data
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning
Information Sciences: an International Journal
Decision trees: a recent overview
Artificial Intelligence Review
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We introduce a novel algorithm for decision tree learning in the multi-instance setting as originally defined by Dietterich et al. It differs from existing multi-instance tree learners in a few crucial, well-motivated details. Experiments on synthetic and real-life datasets confirm the beneficial effect of these differences and show that the resulting system outperforms the existing multi-instance decision tree learners.