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Original Contribution: Stacked generalization
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Democracy in neural nets: voting schemes for classification
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Methods for combining experts' probability assessments
Neural Computation
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A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
The Random Subspace Method for Constructing Decision Forests
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A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Intrusion detection in wireless ad-hoc networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Concept decompositions for large sparse text data using clustering
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The coming age of calm technolgy
Beyond calculation
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
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Neural Networks: A Comprehensive Foundation
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On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving Performance in Neural Networks Using a Boosting Algorithm
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Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers
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Cluster ensembles: a knowledge reuse framework for combining partitionings
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Gait-Based Recognition of Humans Using Continuous HMMs
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Linear and order statistics combiners for reliable pattern classification
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Intrusion detection techniques for mobile wireless networks
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Support Vector Data Description
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Large-Scale Evaluation of Multimodal Biometric Authentication Using State-of-the-Art Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised anomaly detection in network intrusion detection using clusters
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
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Fusion strategies in multimodal biometric verification
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AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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IEEE Transactions on Signal Processing - Part II
The multilayer perceptron as an approximation to a Bayes optimal discriminant function
IEEE Transactions on Neural Networks
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A Data Driven Ensemble Classifier for Credit Scoring Analysis
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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Expert Systems with Applications: An International Journal
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NSPW '09 Proceedings of the 2009 workshop on New security paradigms workshop
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Pruned random subspace method for one-class classifiers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
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Expert Systems with Applications: An International Journal
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Ant colony based approach to predict stock market movement from mood collected on Twitter
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Information Fusion
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Pattern Recognition Letters
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Broad classes of statistical classification algorithms have been developed and applied successfully to a wide range of real-world domains. In general, ensuring that the particular classification algorithm matches the properties of the data is crucial in providing results that meet the needs of the particular application domain. One way in which the impact of this algorithm/application match can be alleviated is by using ensembles of classifiers, where a variety of classifiers (either different types of classifiers or different instantiations of the same classifier) are pooled before a final classification decision is made. Intuitively, classifier ensembles allow the different needs of a difficult problem to be handled by classifiers suited to those particular needs. Mathematically, classifier ensembles provide an extra degree of freedom in the classical bias/variance tradeoff, allowing solutions that would be difficult (if not impossible) to reach with only a single classifier. Because of these advantages, classifier ensembles have been applied to many difficult real-world problems. In this paper, we survey select applications of ensemble methods to problems that have historically been most representative of the difficulties in classification. In particular, we survey applications of ensemble methods to remote sensing, person recognition, one vs. all recognition, and medicine.