C4.5: programs for machine learning
C4.5: programs for machine learning
Trading Accuracy for Simplicity in Decision Trees
Machine Learning
An efficient algorithm for optimal pruning of decision trees
Artificial Intelligence
Machine Learning
A database perspective on knowledge discovery
Communications of the ACM
Machine Learning
Predicting Chemical Parameters of River Water Quality from Bioindicator Data
Applied Intelligence
Building Decision Trees with Constraints
Data Mining and Knowledge Discovery
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A perspective on inductive databases
ACM SIGKDD Explorations Newsletter
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Decision trees for hierarchical multi-label classification
Machine Learning
Clustering Trees with Instance Level Constraints
ECML '07 Proceedings of the 18th European conference on Machine Learning
Stepwise Induction of Multi-target Model Trees
ECML '07 Proceedings of the 18th European conference on Machine Learning
Ensembles of Multi-Objective Decision Trees
ECML '07 Proceedings of the 18th European conference on Machine Learning
Empirical Asymmetric Selective Transfer in Multi-objective Decision Trees
DS '08 Proceedings of the 11th International Conference on Discovery Science
Analysis of time series data with predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Integrating decision tree learning into inductive databases
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Beam search induction and similarity constraints for predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Towards a general framework for data mining
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Incremental multi-target model trees for data streams
Proceedings of the 2011 ACM Symposium on Applied Computing
Hierarchical annotation of medical images
Pattern Recognition
Learning predictive clustering rules
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Tree ensembles for predicting structured outputs
Pattern Recognition
Multi-target regression with rule ensembles
The Journal of Machine Learning Research
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Constrained based inductive systems are a key component of inductive databases and responsible for building the models that satisfy the constraints in the inductive queries. In this paper, we propose a constraint based system for building multi-objective regression trees. A multi-objective regression tree is a decision tree capable of predicting several numeric variables at once. We focus on size and accuracy constraints. By either specifying maximum size or minimum accuracy, the user can trade-off size (and thus interpretability) for accuracy. Our approach is to first build a large tree based on the training data and to prune it in a second step to satisfy the user constraints. This has the advantage that the tree can be stored in the inductive database and used for answering inductive queries with different constraints. Besides size and accuracy constraints, we also briefly discuss syntactic constraints. We evaluate our system on a number of real world data sets and measure the size versus accuracy trade-off.