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» On Combining Classifiers
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FUZZIEEE
2007
IEEE
15 years 3 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
15 years 1 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
16 years 1 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»
16 years 8 days 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
15 years 3 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...