C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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
Improving the performance of an incremental algorithm driven by error margins
Intelligent Data Analysis - Knowledge Discovery from Data Streams
Incremental learning with multiple classifier systems using correction filters for classification
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
A novel local patch framework for fixing supervised learning models
Proceedings of the 21st ACM international conference on Information and knowledge management
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Incremental learning is a good approach for classification when data-sets are too large or when new examples can arrive at any time. Forgetting these examples while keeping only the relevant information lets us reduce memory requirements. The algorithm presented in this paper, called IADEM, has been developed using these approaches and other concepts such as Chernoff and Hoeffding bounds. The most relevant features of this new algorithm are: its capability to deal with datasets of any size for inducing accurate trees and its capacity to keep updated the estimation error of the tree that is being induced. This estimation of the error is fundamental to satisfy the user requirements about the desired error in the tree and to detect noise in the datasets.