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The third is a novel heuristic feature selection algorithm with effectiveness but without overfitting problem. Experimental results convince our model acquires ...
Abstract: With a deeper investigation to deciphering the sophisticated relations among input and output variables of multi-class classification problems, ...
Our model devotes to three accomplishments of multi-class classification tasks. Feature discretization using fuzzy clustering analysis for the improvement of ...
Abstract. This paper deals with supervised classification and feature selection with application in the context of high dimensional features.
Feature selection is an essential problem for pattern classification systems. This paper studies how to provide systems with the most characterizing ...
May 17, 2021 · A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging ...
Missing: Heuristic Efficiency
Clustering is a popular technique for discovering groups of similar objects in large datasets. It is nowadays applied in all areas of life sciences, from ...
A feature selection method based on multiple feature subsets extraction and result fusion for improving classification performance. 2024, Applied Soft Computing.
Aug 30, 2022 · For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic ...
The feature selection problem consists of finding a subset of features that represents the original dataset with the aim of eliminating irrelevant and redundant ...