C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
The application of AdaBoost for distributed, scalable and on-line learning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Online Ensemble Learning: An Empirical Study
Machine Learning
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Decision trees for mining data streams
Intelligent Data Analysis
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Improving the performance of an incremental algorithm driven by error margins
Intelligent Data Analysis - Knowledge Discovery from Data Streams
Incremental algorithm driven by error margins
DS'06 Proceedings of the 9th international conference on Discovery Science
Constructing rough decision forests
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Induction of decision trees using an internal control of induction
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Classification is a quite relevant task within data mining area. This task is not trivial and some difficulties can arise depending on the nature of the problem. Multiple classifier systems have been used to construct ensembles of base classifiers in order to solve or alleviate some of those problems. One of the most current problems that is being studied in recent years is how to learn when the datasets are too large or when new information can arrive at any time. In that case, incremental learning is an approach that can be used. Some works have used multiple classifier system to learn in an incremental way and the results are very promising. The aim of this paper is to propose a method for improving the classification (or prediction) accuracy reached by multiple classifier systems in this context.