Incremental induction of topologically minimal trees
Proceedings of the seventh international conference (1990) on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning in the presence of concept drift and hidden contexts
Machine Learning
Efficient incremental induction of decision trees
Machine Learning
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decision Tree Induction Based on Efficient Tree Restructuring
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On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
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Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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Mining time-changing data streams
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A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
An Analysis of Functional Trees
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Unifeid Bias-Variance Decomposition and its Applications
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Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth 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
Machine Learning
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Using multiple windows to track concept drift
Intelligent Data Analysis
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Preface: an overview on learning from data streams
New Generation Computing
Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Ambiguous decision trees for mining concept-drifting data streams
Pattern Recognition Letters
Regression Trees from Data Streams with Drift Detection
DS '09 Proceedings of the 12th International Conference on Discovery Science
Tracking Recurring Concepts with Meta-learners
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Incremental learning with multiple classifier systems using correction filters for classification
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
EDLRT: Entropy-based dummy variables logistic regression tree
Intelligent Data Analysis
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ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Very Fast Decision Rules for multi-class problems
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Learning decision rules from data streams
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Handling time changing data with adaptive very fast decision rules
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Decision trees: a recent overview
Artificial Intelligence Review
Learning from data streams with only positive and unlabeled data
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A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
The CART decision tree for mining data streams
Information Sciences: an International Journal
Classifying evolving data streams with partially labeled data
Intelligent Data Analysis
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In this paper we study the problem of constructing accurate decision tree models from data streams. Data streams are incremental tasks that require incremental, online, and any-time learning algorithms. One of the most successful algorithms for mining data streams is VFDT. We have extended VFDT in three directions: the ability to deal with continuous data; the use of more powerful classification techniques at tree leaves, and the ability to detect and react to concept drift. VFDTc system can incorporate and classify new information online, with a single scan of the data, in time constant per example. The most relevant property of our system is the ability to obtain a performance similar to a standard decision tree algorithm even for medium size datasets. This is relevant due to the any-time property. We also extend VFDTc with the ability to deal with concept drift, by continuously monitoring differences between two class-distribution of the examples: the distribution when a node was built and the distribution in a time window of the most recent examples. We study the sensitivity of VFDTc with respect to drift, noise, the order of examples, and the initial parameters in different problems and demonstrate its utility in large and medium data sets.