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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 …
On the joint-effect of class imbalance and overlap: a critical review
Current research on imbalanced data recognises that class imbalance is aggravated by
other data intrinsic characteristics, among which class overlap stands out as one of the most …
other data intrinsic characteristics, among which class overlap stands out as one of the most …
ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment,
and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from …
and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from …
Comparing the behavior of oversampling and undersampling approach of class imbalance learning by combining class imbalance problem with noise
Class imbalance learning is a recent topic, which helps us to detect the classes from
unbalanced datasets. In various real scenarios, where we need to find the exceptional cases …
unbalanced datasets. In various real scenarios, where we need to find the exceptional cases …
[HTML][HTML] Modeling the organic matter of water using the decision tree coupled with bootstrap aggregated and least-squares boosting
The purpose of the work is to investigate the use of decision tree (DT) enhanced by
bootstrap aggregates (Bag) and least-squares boosting (Lsboost) in modeling the organic …
bootstrap aggregates (Bag) and least-squares boosting (Lsboost) in modeling the organic …
Enhancing Multiclass Classification in FARC-HD Fuzzy Classifier: On the Synergy Between -Dimensional Overlap Functions and Decomposition Strategies
There are many real-world classification problems involving multiple classes, eg, in
bioinformatics, computer vision, or medicine. These problems are generally more difficult …
bioinformatics, computer vision, or medicine. These problems are generally more difficult …
Dynamic ensemble selection for multi-class classification with one-class classifiers
In this paper we deal with the problem of addressing multi-class problems with
decomposition strategies. Based on the divide-and-conquer principle, a multi-class problem …
decomposition strategies. Based on the divide-and-conquer principle, a multi-class problem …
Fuzzy rule-based classification systems for multi-class problems using binary decomposition strategies: on the influence of n-dimensional overlap functions in the …
Multi-class classification problems appear in a broad variety of real-world problems, eg,
medicine, genomics, bioinformatics, or computer vision. In this context, decomposition …
medicine, genomics, bioinformatics, or computer vision. In this context, decomposition …
Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data
Multi-class imbalance classification problems occur in many real-world applications, which
suffer from the quite different distribution of classes. Decomposition strategies are well …
suffer from the quite different distribution of classes. Decomposition strategies are well …
META-DES. Oracle: Meta-learning and feature selection for dynamic ensemble selection
Dynamic ensemble selection (DES) techniques work by estimating the competence level of
each classifier from a pool of classifiers, and selecting only the most competent ones for the …
each classifier from a pool of classifiers, and selecting only the most competent ones for the …