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Graph-Based Feature Selection Approach for Molecular Activity Prediction
(ACS, 2022)
In the construction of QSAR models for the prediction of molecular activity, feature selection is a common task aimed at improving the results and understanding of the problem. The selection of features allows elimination ...
Local-based k values for multi-label k-nearest neighbors rule
(Elsevier, 2022)
Multi-label learning is a growing field in machine learning research. Many applications address instances that simultaneously belong to many categories, which cannot be disregarded if optimal results are desired. Among the ...
SI(FS)2: Fast simultaneous instance and feature selection for datasets with many features
(Elsevier, 2021)
Data reduction is becoming increasingly relevant due to the enormous amounts of data that are constantly being produced in many fields of research. Instance selection is one of the most widely used methods for this task. ...
Instance selection for multi-label learning based on a scalable evolutionary algorithm
(IEEE, 2021)
Multi-label classification has recently attracted
greater research interest as a data mining task. Many current ap-
plications in data mining address problems having instances that
belong to more than one class, which ...
Graph-Based Feature Selection Approach for Molecular Activity Prediction
(American Chemical Society, 2022)
In the construction of QSAR models for the prediction of molecular
activity, feature selection is a common task aimed at improving the results and
understanding of the problem. The selection of features allows elimination ...
Effective Feature Selection Method for Class-Imbalance Datasets Applied to Chemical Toxicity Prediction
(American Chemical Society, 2021)
During the drug development process, it is common to carry out toxicity tests and adverse effect studies, which are essential to guarantee patient safety and the success of the research. The use of in silico quantitative ...
Cooperative coevolutionary instance selection for multilabel problems
(Elsevier, 2021)
Multilabel classification as a data mining task has recently attracted greater research interest. Many
current data mining applications address problems having instances that belong to more than one
class, which requires ...