C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
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
Machine Learning
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The Biases of Decision Tree Pruning Strategies
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Moderated VFDT in stream mining using adaptive tie threshold and incremental pruning
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
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Decision tree learning can be roughly classified into two categories: static and incremental inductions. Static tree induction applies greedy search in splitting test for obtaining a global optimal model. Incremental tree induction constructs a decision model by analyzing data in short segments; during each segment a local optimal tree structure is formed. Very Fast Decision Tree [4] is a typical incremental tree induction based on the principle of Hoeffding bound for node-splitting test. But it does not work well under noisy data. In this paper, we propose a new incremental tree induction model called incrementally Optimized Very Fast Decision Tree (iOVFDT), which uses a multi-objective incremental optimization method. iOVFDT also integrates four classifiers at the leaf levels. The proposed incremental tree induction model is tested with a large volume of data streams contaminated with noise. Under such noisy data, we investigate how iOVFDT that represents incremental induction method working with local optimums compares to C4.5 which loads the whole dataset for building a globally optimal decision tree. Our experiment results show that iOVFDT is able to achieve similar though slightly lower accuracy, but the decision tree size and induction time are much smaller than that of C4.5.