C4.5: programs for machine learning
C4.5: programs for machine learning
Learning in the presence of concept drift and hidden contexts
Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Machine Learning
Incremental Learning from Noisy Data
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on 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 with partial instance memory
Artificial Intelligence
Decision trees for mining data streams
Intelligent Data Analysis
Guide to Intelligent Data Analysis: How to Intelligently Make Sense of Real Data
Guide to Intelligent Data Analysis: How to Intelligently Make Sense of Real Data
The Journal of Machine Learning Research
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
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Decision rules are one of the most interpretable and flexible models for data mining prediction tasks. Till now, few works presented online, any-time and one-pass algorithms for learning decision rules in the stream mining scenario. A quite recent algorithm, the Very Fast Decision Rules (VFDR), learns set of rules, where each rule discriminates one class from all the other. In this work we extend the VFDR algorithm by decomposing a multi-class problem into a set of two-class problems and inducing a set of discriminative rules for each binary problem. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classifiers, processing each example once. Moreover, it is able to learn ordered and unordered rule sets. The new approach is evaluated on various real and artificial datasets. The new algorithm improves the performance of the previous version and is competitive with the state-of-the-art decision tree learning method for data streams.