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
Relational Data Mining
Handling Real Numbers in ILP: A Step Towards Better Behavioural Clones (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Stochastic Propositionalization of Non-determinate Background Knowledge
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Experiments in Predicting Biodegradability
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Multiple instance learning of real valued data
The Journal of Machine Learning Research
ICML '05 Proceedings of the 22nd international conference on Machine learning
Combining model-based and instance-based learning for first order regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
An integrated approach to feature invention and model construction for drug activity prediction
Proceedings of the 24th international conference on Machine learning
ReMauve: A Relational Model Tree Learner
Inductive Logic Programming
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(Multi-)relational regression consists of predicting continuous response of target objects called reference objects by taking into account interactions with other objects called task-relevant objects. In relational databases, reference objects and task-relevant objects are stored in distinct data relations. Interactions between objects are expressed by means of (many-to-one) foreign key constraints which may allow linking explanatory variables of a task-relevant object in several alternative ways to the response variable. By materializing multiple assignments in distinct attribute-value vectors, a reference object is represented as a bag of multiple instances, although there is only one response value for the entire bag. This works points out the same assumption of multi-instance learning that is a primary instance is responsible for the observed response value of a reference object. We propose a top-down induction multi-relational model tree system which navigates foreign key constraints according to a divide-and-conquer strategy, derives a representation of reference objects as bags of attribute-value vectors and then, for each bag, constructs a primary instance as main responsible of the response value. Coefficients of local hyperplane are estimated in an EM implementation of the stepwise least square regression. Experiments confirm the improved accuracy of our proposal with respect to traditional attribute-value and relational model tree learners.