C4.5: programs for machine learning
C4.5: programs for machine learning
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Noise modelling and evaluating learning from examples
Artificial Intelligence
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth 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
PROLOG Programming for Artificial Intelligence
PROLOG Programming for Artificial Intelligence
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
Mining Temporal Features in Association Rules
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Refined Time Stamps for Concept Drift Detection During Mining for Classification Rules
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Artificial recurrence for classification of streaming data with concept shift
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Learning curve in concept drift while using active learning paradigm
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
A survey of multiple classifier systems as hybrid systems
Information Fusion
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Many of today's real world domains require online classification tasks in very demanding situations. This work presents the results of applying the CD3 algorithm to telecommunications call data. CD3 enables the detection of concept drift in the presence of noise within real time data. The application detects the drift using a TSAR methodology and applies a purging mechanism as a corrective action. The main focus of this work is to identify from customer files and call records if the profile of customers registering for a 'friends and family' service is changing over a period of time. We will begin with a review of the CD3 application and the presentation of the data. This will conclude with experimental results.