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
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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
Proceedings of the 2004 ACM symposium on Applied computing
Decision Tree Evolution Using Limited Number of Labeled Data Items from Drifting Data Streams
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Learning decision trees from dynamic data streams
Proceedings of the 2005 ACM symposium on Applied computing
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
ACM SIGMOD Record
Decision trees for mining data streams
Intelligent Data Analysis
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Predicting link quality using supervised learning in wireless sensor networks
ACM SIGMOBILE Mobile Computing and Communications Review
Collaborative filtering on streaming data with interest-drifting
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Knowledge maintenance on data streams with concept drifting
CIS'04 Proceedings of the First international conference on Computational and Information Science
Learning very fast decision tree from uncertain data streams with positive and unlabeled samples
Information Sciences: an International Journal
A new method of mining data streams using harmony search
Journal of Intelligent Information Systems
Decision trees: a recent overview
Artificial Intelligence Review
The CART decision tree for mining data streams
Information Sciences: an International Journal
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In real world situations, explanations for the same observations may be different depending on perceptions or contexts. They may change with time especially when concept drift occurs. This phenomenon incurs ambiguities. It is useful if an algorithm can learn to reflect ambiguities and select the best decision according to context or situation. Based on this viewpoint, we study the problem of deriving ambiguous decision trees from data streams to cope with concept drift. CVFDT (Concept-adapting Very Fast Decision Tree) is one of the most well-known streaming data mining methods that can learn decision trees incrementally. In this paper, we establish a method called ambiguous CVFDT (aCVFDT), which integrates ambiguities into CVFDT by exploring multiple options at each node whenever a node is to be split. When aCVFDT is used to make class predictions, it is guaranteed that the best and newest knowledge is used. When old concepts recur, aCVFDT can immediately relearn them by using the corresponding options recorded at each node. Furthermore, CVFDT does not automatically detect occurrences of concept drift and only scans trees periodically, whereas an automatic concept drift detecting mechanism is used in aCVFDT. In our experiments, hyperplane problem and two benchmark problems from the UCI KDD Archive, namely Network Intrusion and Forest CoverType, are used to validate the performance of aCVFDT. The experimental results show that aCVFDT obtains significantly improved results over traditional CVFDT.