Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Using Model Trees for Classification
Machine Learning
Interactive machine learning: letting users build classifiers
International Journal of Human-Computer Studies
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Two Strategies of Adaptive Cluster Covering with Descent and Their Comparison to Other Algorithms
Journal of Global Optimization
Optimizing the Induction of Alternating Decision Trees
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Generalization and decision tree induction: efficient classification in data mining
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
2006 Special issue: Machine learning in sedimentation modelling
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Experiments with AdaBoost.RT, an improved boosting scheme for regression
Neural Computation
Adaptive mixtures of local experts
Neural Computation
IEEE Transactions on Fuzzy Systems
2006 Special issue: Machine learning in sedimentation modelling
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Model-based monitoring for early warning flood detection
Proceedings of the 6th ACM conference on Embedded network sensor systems
A New Cluster Based Fuzzy Model Tree for Data Modeling
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Fault detection in water supply systems using hybrid (theory and data-driven) modelling
Mathematical and Computer Modelling: An International Journal
Optimizing biodiversity prediction from abiotic parameters
Environmental Modelling & Software
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Modular model is a particular type of committee machine and is comprised of a set of specialized (local) models each of which is responsible for a particular region of the input space, and may be trained on a subset of training set. Many algorithms for allocating such regions to local models typically do this in automatic fashion. In forecasting natural processes, however, domain experts want to bring in more knowledge into such allocation, and to have certain control over the choice of models. This paper presents a number of approaches to building modular models based on various types of splits of training set and combining the models' outputs (hard splits, statistically and deterministically driven soft combinations of models, 'fuzzy committees', etc.). An issue of including a domain expert into the modeling process is also discussed, and new algorithms in the class of model trees (piece-wise linear modular regression models) are presented. Comparison of the algorithms based on modular local modeling to the more traditional 'global' learning models on a number of benchmark tests and river flow forecasting problems shows their higher accuracy and transparency of the resulting models.