Journal ArticleDOI
A probabilistic model of classifier competence for dynamic ensemble selection
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TLDR
The results obtained indicate that the full vector of class supports should be used for evaluating the classifier competence as this potentially improves performance of MCSs.About:
This article is published in Pattern Recognition.The article was published on 2011-10-01. It has received 175 citations till now. The article focuses on the topics: Margin classifier & Quadratic classifier.read more
Citations
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Journal ArticleDOI
A survey of multiple classifier systems as hybrid systems
TL;DR: An up-to-date survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems is presented, providing a vision of the spectrum of applications that are currently being developed.
Journal ArticleDOI
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
TL;DR: The study of Baesens et al. (2003) is updated and several novel classification algorithms to the state-of-the-art in credit scoring are compared, providing an independent assessment of recent scoring methods and offering a new baseline to which future approaches can be compared.
Journal ArticleDOI
ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
Oskar Maier,Bjoern H. Menze,Janina von der Gablentz,Levin Häni,Mattias P. Heinrich,Matthias Liebrand,Stefan Winzeck,Abdul Basit,Paul Bentley,Liang Chen,Daan Christiaens,Francis Dutil,Karl Egger,Chaolu Feng,Ben Glocker,Michael Götz,Tom Haeck,Hanna-Leena Halme,Hanna-Leena Halme,Mohammad Havaei,Khan M. Iftekharuddin,Pierre-Marc Jodoin,Konstantinos Kamnitsas,Elias Kellner,Antti Korvenoja,Hugo Larochelle,Christian Ledig,Jia-Hong Lee,Frederik Maes,Qaiser Mahmood,Qaiser Mahmood,Klaus H. Maier-Hein,Richard McKinley,John Muschelli,Chris Pal,Linmin Pei,Janaki Raman Rangarajan,Syed M. S. Reza,David Robben,Daniel Rueckert,Eero Salli,Paul Suetens,Ching-Wei Wang,Matthias Wilms,Jan S. Kirschke,Ulrike M. Krämer,Thomas F. Münte,Peter Schramm,Roland Wiest,Heinz Handels,Mauricio Reyes +50 more
TL;DR: This paper proposes a common evaluation framework for automatic stroke lesion segmentation from MRIP, describes the publicly available datasets, and presents the results of the two sub‐challenges: Sub‐Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES).
Journal ArticleDOI
Dynamic classifier selection
TL;DR: An updated taxonomy of Dynamic Selection techniques is proposed based on the main characteristics found in a dynamic selection system, and an extensive experimental analysis, considering a total of 18 state-of-the-art dynamic selection techniques, as well as static ensemble combination and single classification models.
Journal ArticleDOI
Dynamic selection of classifiers-A comprehensive review
TL;DR: This comprehensive study observed that, for some classification problems, the performance contribution of the dynamic selection approach is statistically significant when compared to that of a single-based classifier and found evidence of a relation between the observed performance contribution and the complexity of the classification problem.
References
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Journal ArticleDOI
Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Journal ArticleDOI
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Journal Article
Statistical Comparisons of Classifiers over Multiple Data Sets
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.
Book
Solving least squares problems
TL;DR: Since the lm function provides a lot of features it is rather complicated so it is going to instead use the function lsfit as a model, which computes only the coefficient estimates and the residuals.