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» On Combining Classifiers
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FUZZIEEE
2007
IEEE
14 years 2 months ago
Evolving Single- and Multi-Model Fuzzy Classifiers with FLEXFIS-Class
Abstract-- In this paper a new method for training singlemodel and multi-model fuzzy classifiers incrementally and adaptively is proposed, which is called FLEXFIS-Class. The evolvi...
Edwin Lughofer, Plamen P. Angelov, Xiaowei Zhou
CEC
2010
IEEE
13 years 11 months ago
Multi-objective memetic evolution of ART-based classifiers
In this paper we present a novel framework for evolving ART-based classification models, which we refer to as MOME-ART. The new training framework aims to evolve populations of ART...
Rong Li, Timothy R. Mersch, Oriana X. Wen, Assem K...
ICIP
2002
IEEE
14 years 11 months ago
Hybrid and parallel face classifier based on artificial neural networks and principal component analysis
We present a hybrid and parallel system based on artificial neural networks for a face invariant classifier and general pattern recognition problems. A set of face features is ext...
Peter V. Bazanov, Tae-Kyun Kim, Seok-Cheol Kee, Sa...
KDD
2002
ACM
119views Data Mining» more  KDD 2002»
14 years 10 months ago
Evaluating classifiers' performance in a constrained environment
In this paper, we focus on methodology of finding a classifier with a minimal cost in presence of additional performance constraints. ROCCH analysis, where accuracy and cost are i...
Anna Olecka
CIARP
2007
Springer
14 years 2 months ago
Bagging with Asymmetric Costs for Misclassified and Correctly Classified Examples
Abstract. Diversity is a key characteristic to obtain advantages of combining predictors. In this paper, we propose a modification of bagging to explicitly trade off diversity and ...
Ricardo Ñanculef, Carlos Valle, Héct...