Employing linear regression in regression tree leaves
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Machine Learning - special issue on inductive logic programming
Inducing classification and regression trees in first order logic
Relational Data Mining
Functional Models for Regression Tree Leaves
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Multi-relational Decision Tree Induction
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Involving Aggregate Functions in Multi-relational Search
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Probabilistic Relational Models
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Top-Down Induction of Model Trees with Regression and Splitting Nodes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A simple regression based heuristic for learning model trees
Intelligent Data Analysis
Top-down induction of first-order logical decision trees
Artificial Intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Refining aggregate conditions in relational learning
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Numbers in multi-relational data mining
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Top-Down Induction of Relational Model Trees in Multi-instance Learning
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
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Model trees are a special case of regression trees in which linear regression models are constructed in the leaves. Little attention has been paid to model trees in relational learning, mainly because the task of learning linear regression equations in this context involves dealing with non-determinacy of predictive attributes. Whereas existing approaches handle this non-determinacy issue either by selecting a single value or by aggregating over all values, in this paper we present a model tree learning system that combines both.