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 …
An empirical comparison on state-of-the-art multi-class imbalance learning algorithms and a new diversified ensemble learning scheme
J Bi, C Zhang - Knowledge-Based Systems, 2018 - Elsevier
Class-imbalance learning is one of the most challenging problems in machine learning. As a
new and important direction in this field, multi-class imbalanced data classification has …
new and important direction in this field, multi-class imbalanced data classification has …
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 …
Dynamic ensemble learning for multi-label classification
Ensemble learning has been shown to be an effective approach to solve multi-label
classification problem. However, most existing ensemble learning methods do not consider …
classification problem. However, most existing ensemble learning methods do not consider …
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 study on combining dynamic selection and data preprocessing for imbalance learning
In real life, classifier learning may encounter a dataset in which the number of instances of a
given class is much higher than for other classes. Such imbalanced datasets require special …
given class is much higher than for other classes. Such imbalanced datasets require special …