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Dynamic classifier selection: Recent advances and perspectives
Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …
Dynamic selection of classifiers—a comprehensive review
This work presents a literature review of multiple classifier systems based on the dynamic
selection of classifiers. First, it briefly reviews some basic concepts and definitions related to …
selection of classifiers. First, it briefly reviews some basic concepts and definitions related to …
The choice of scaling technique matters for classification performance
Dataset scaling, also known as normalization, is an essential preprocessing step in a
machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary …
machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary …
A survey of multiple classifier systems as hybrid systems
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 …
classifier systems, which can be built following either the same or different models and/or …
[ספר][B] Combining pattern classifiers: methods and algorithms
LI Kuncheva - 2014 - books.google.com
A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …
pattern recognition to ensemble feature selection, now in its second edition The art and …
[ספר][B] Conformal prediction for reliable machine learning: theory, adaptations and applications
V Balasubramanian, SS Ho, V Vovk - 2014 - books.google.com
The conformal predictions framework is a recent development in machine learning that can
associate a reliable measure of confidence with a prediction in any real-world pattern …
associate a reliable measure of confidence with a prediction in any real-world pattern …
A novel heterogeneous ensemble credit scoring model based on bstacking approach
In recent years, credit scoring has become an efficient tool that allows financial institutions to
differentiate their potential default borrowers. Accordingly, researchers have developed a …
differentiate their potential default borrowers. Accordingly, researchers have developed a …
Classifier selection for majority voting
Individual classification models are recently challenged by combined pattern recognition
systems, which often show better performance. In such systems the optimal set of classifiers …
systems, which often show better performance. In such systems the optimal set of classifiers …
META-DES: A dynamic ensemble selection framework using meta-learning
Dynamic ensemble selection systems work by estimating the level of competence of each
classifier from a pool of classifiers. Only the most competent ones are selected to classify a …
classifier from a pool of classifiers. Only the most competent ones are selected to classify a …
[ספר][B] Machine learning and data mining
I Kononenko, M Kukar - 2007 - books.google.com
Data mining is often referred to by real-time users and software solutions providers as
knowledge discovery in databases (KDD). Good data mining practice for business …
knowledge discovery in databases (KDD). Good data mining practice for business …