The weighted majority algorithm
Information and Computation
Learning in the presence of concept drift and hidden contexts
Machine Learning
Combining Classifiers with Meta Decision Trees
Machine Learning
Online Ensemble Learning: An Empirical Study
Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Boosting classifiers for drifting concepts
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Probabilistic aggregation of classifiers for incremental learning
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Knowledge-Based sampling for subgroup discovery
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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We present a model based on ensemble of base classifiers, that are combined using weighted majority voting, for the task of incremental classification. Definition of such voting weights becomes even more critical in non-stationary environments where the patterns underlying the observations change over time. Given an instance to classify, we propose to define each voting weight as a function that will take into account the location of an instance to classify in the different class-specific feature spaces and also the prior probability of such classes given the knowledge represented by the classifier as well as its overall performance in learning its training examples. This approach can improve the generalization performance and ability to control the stability/plasticity tradeoff, in stationary and non-stationary environments. Experiments were carried out using several real classification problems already introduced to test incremental algorithms in stationary as well as non-stationary environments.