A literature survey and empirical study of meta-learning for classifier selection
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 selection of appropriate classification algorithm for a particular problem is a challenging …
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 …
Trusting my predictions: on the value of Instance-Level analysis
Machine Learning solutions have spread along many domains, including critical
applications. The development of such models usually relies on a dataset containing …
applications. The development of such models usually relies on a dataset containing …
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 …
[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 …
effectiveness as well as minimizing the training time. An additional advantage is the ability to …
OLP++: An online local classifier for high dimensional data
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 …
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
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 …
perceived competence in labeling each given query instance in particular. To do so, most …
A new ensemble learning method based on learning automata
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 …
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 …
prompt detection of faults and thus help enhance the overall reliability and quality of the …
Measuring instance hardness using data complexity measures
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 …
which may leverage advances in Machine Learning. In classification problems, an instance …