Employing linear regression in regression tree leaves
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Neural networks and the bias/variance dilemma
Neural Computation
Constructive incremental learning from only local information
Neural Computation
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SECRET: a scalable linear regression tree algorithm
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Learning of Linear Model Trees
Machine Learning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Regression Trees from Data Streams with Drift Detection
DS '09 Proceedings of the 12th International Conference on Discovery Science
Learning model trees from evolving data streams
Data Mining and Knowledge Discovery
Incremental multi-target model trees for data streams
Proceedings of the 2011 ACM Symposium on Applied Computing
Fast perceptron decision tree learning from evolving data streams
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Incremental linear model trees on massive datasets: keep it simple, keep it fast
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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In this paper we propose a fast and incremental algorithm for learning model trees from data streams (FIMT) for regression problems. The algorithm is incremental, works online, processes examples once at the speed they arrive, and maintains an any-time regression model. The leaves contain linear-models trained online from the examples that fall at that leaf, a process with low complexity. The use of linear models in the leaves increases its any-time global performance. FIMT is able to obtain competitive accuracy with batch learners even for medium size datasets, but with better training time in an order of magnitude. We study the properties of FIMT over several artificial and real datasets and evaluate its sensitivity on the order of examples and the noise level.