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 …
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and …
Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …
successful techniques in machine learning. Recently, the number of ensemble-based …
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 …
[图书][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 …
Dynamic ensemble selection for multi-class imbalanced datasets
Many real-world classification tasks suffer from the class imbalanced problem, in which
some classes are highly underrepresented as compared to other classes. In this paper, we …
some classes are highly underrepresented as compared to other classes. In this paper, we …
[HTML][HTML] Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis
GW Stachowiak, M Wolski, T Woloszynski… - Biosurface and …, 2016 - Elsevier
Current assessment of osteoarthritis (OA) is primary based on visual grading of joint space
narrowing and osteophytes present on radiographs. The approach is observer-dependent …
narrowing and osteophytes present on radiographs. The approach is observer-dependent …
A novel dynamic ensemble selection classifier for an imbalanced data set: An application for credit risk assessment
Credit risk assessment is usually regarded as an imbalanced classification task solved by
static ensemble classifiers. However, the dynamic ensemble selection (DES) strategy that …
static ensemble classifiers. However, the dynamic ensemble selection (DES) strategy that …
A framework for cardiac arrhythmia detection from IoT-based ECGs
Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that
causes approximately 12% of all deaths globally. The development of Internet-of-Things has …
causes approximately 12% of all deaths globally. The development of Internet-of-Things has …
DESlib: A Dynamic ensemble selection library in Python
DESlib is an open-source python library providing the implementation of several dynamic
selection techniques. The library is divided into three modules:(i) dcs, containing the …
selection techniques. The library is divided into three modules:(i) dcs, containing the …
A semantics aware random forest for text classification
The Random Forest (RF) classifiers are suitable for dealing with the high dimensional noisy
data in text classification. An RF model comprises a set of decision trees each of which is …
data in text classification. An RF model comprises a set of decision trees each of which is …