Related references
Note: Only part of the references are listed.Evolved feature weighting for random subspace classifier
Loris Nanni et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2008)
A general wrapper approach to selection of class-dependent features
Lipo Wang et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2008)
Artificial neural networks with evolutionary instance selection for financial forecasting
KJ Kim
EXPERT SYSTEMS WITH APPLICATIONS (2006)
Accuracy/diversity and ensemble MLP classifier design
Terry Windeatt
IEEE TRANSACTIONS ON NEURAL NETWORKS (2006)
Large margin nearest neighbor classifiers
C Domeniconi et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2005)
Design of nearest neighbor classifiers: multi-objective approach
JH Chen et al.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING (2005)
Is combining classifiers with stacking better than selecting the best one?
S Dzeroski et al.
MACHINE LEARNING (2004)
Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study
JR Cano et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2003)
Online ensemble learning: An empirical study
A Fern et al.
MACHINE LEARNING (2003)
Advances in instance selection for instance-based learning algorithms
H Brighton et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2002)
Shape matching and object recognition using shape contexts
S Belongie et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2002)
Learn++: An incremental learning algorithm for supervised neural networks
R Polikar et al.
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS (2001)
MultiBoosting: A technique for combining boosting and wagging
GI Webb
MACHINE LEARNING (2000)
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
TG Dietterich
MACHINE LEARNING (2000)
On the algorithmic implementation of stochastic discrimination
EM Kleinberg
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2000)
Reduction techniques for instance-based learning algorithms
DR Wilson et al.
MACHINE LEARNING (2000)