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A survey of multiple classifier systems as hybrid systems
TLDR
A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets. Expand
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Ensemble learning for data stream analysis: A survey
TLDR
A comprehensive survey of ensemble approaches for data stream analysis.Taxonomy of ensemble algorithms for various data stream mining tasks.Discussion of open research problems. Expand
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Cost-sensitive decision tree ensembles for effective imbalanced classification
TLDR
In this paper, we introduce an effective ensemble of cost-sensitive decision trees for imbalanced classification. Expand
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Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets
TLDR
We present a study on oversampling for multi-class imbalanced datasets that focuses on the analysis of the class characteristics. Expand
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Fault diagnosis of marine 4-stroke diesel engines using a one-vs-one extreme learning ensemble
TLDR
This paper proposes a novel approach for intelligent fault diagnosis for stroke Diesel marine engines, which are commonly used in on-road and marine transportation. Expand
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Diversity measures for one-class classifier ensembles
TLDR
One-class classification is one of the most challenging topics in contemporary machine learning and not much attention had been paid to the task of creating efficient one-class ensembles. Expand
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Combined classifier based on feature space partitioning
TLDR
This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. Expand
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Drift Detection and Model Selection Algorithms: Concept and Experimental Evaluation
TLDR
The traditional classifier cannot keep its quality, when the concept drift appears. Expand
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Computer recognition systems
TLDR
The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. Expand
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