A literature survey and empirical study of meta-learning for classifier selection

I Khan, X Zhang, M Rehman, R Ali - IEEE Access, 2020 - ieeexplore.ieee.org
Classification is the key and most widely studied paradigm in machine learning community.
The selection of appropriate classification algorithm for a particular problem is a challenging …

The choice of scaling technique matters for classification performance

LBV de Amorim, GDC Cavalcanti, RMO Cruz - Applied Soft Computing, 2023 - Elsevier
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 …

Trusting my predictions: on the value of Instance-Level analysis

AC Lorena, PYA Paiva, RBC Prudêncio - ACM Computing Surveys, 2024 - dl.acm.org
Machine Learning solutions have spread along many domains, including critical
applications. The development of such models usually relies on a dataset containing …

DESlib: A Dynamic ensemble selection library in Python

RMO Cruz, LG Hafemann, R Sabourin… - Journal of Machine …, 2020 - jmlr.org
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 …

[HTML][HTML] A hybridization of distributed policy and heuristic augmentation for improving federated learning approach

D Połap, M Woźniak - Neural Networks, 2022 - Elsevier
Modifying the existing models of classifiers' operation is primarily aimed at increasing the
effectiveness as well as minimizing the training time. An additional advantage is the ability to …

OLP++: An online local classifier for high dimensional data

MA Souza, R Sabourin, GDC Cavalcanti, RMO Cruz - Information Fusion, 2023 - Elsevier
Ensemble diversity is an important characteristic of Multiple Classifier Systems (MCS), which
aim at improving the overall performance of a classification system by combining the …

A dynamic multiple classifier system using graph neural network for high dimensional overlapped data

MA Souza, R Sabourin, GDC Cavalcanti, RMO Cruz - Information Fusion, 2024 - Elsevier
Dynamic selection techniques select a subset of the classifiers from a pool according to their
perceived competence in labeling each given query instance in particular. To do so, most …

A new ensemble learning method based on learning automata

M Savargiv, B Masoumi, MR Keyvanpour - Journal of Ambient Intelligence …, 2022 - Springer
Improving the performance of machine learning algorithms has been always the topic of
interest in data mining. The ensemble learning is one of the machine learning methods that …

Software fault prediction based on the dynamic selection of learning technique: findings from the eclipse project study

SS Rathore, S Kumar - Applied Intelligence, 2021 - Springer
An effective software fault prediction (SFP) model could help developers in the quick and
prompt detection of faults and thus help enhance the overall reliability and quality of the …

Measuring instance hardness using data complexity measures

JLM Arruda, RBC Prudêncio, AC Lorena - … 23, 2020, Proceedings, Part II 9, 2020 - Springer
Assessing the hardness of each instance in a problem is an important meta-knowledge
which may leverage advances in Machine Learning. In classification problems, an instance …