An ensemble method for incremental classification in stationary and non-stationary environments

Ricardo Ñanculef, Erick López, Héctor Allende, Héctor Allende-Cid

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


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.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Proceedings
Número de páginas8
EstadoPublicada - 2011
Publicado de forma externa
Evento16th Iberoamerican Congress on Pattern Recognition, CIARP 2011 - Pucon, Chile
Duración: 15 nov. 201118 nov. 2011

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen7042 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


Conferencia16th Iberoamerican Congress on Pattern Recognition, CIARP 2011


Profundice en los temas de investigación de 'An ensemble method for incremental classification in stationary and non-stationary environments'. En conjunto forman una huella única.

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