Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/54973
Title: A Novel Approach to Increase the Efficiency of Filter-Based Feature Selection Methods in High-Dimensional Datasets With Strong Correlation Structure
Authors: Akoğul, Serkan
Keywords: Feature selection
filter feature selection
Gaussian mixture model (GMM)
Gaussian mixture discriminant analysis (GMDA)
Discriminant-Analysis
Maximum-Likelihood
Classification
Publisher: Ieee-Inst Electrical Electronics Engineers Inc
Abstract: Nowadays, data dimensions have increased depending on the developments in information and measurement technologies. Due to the high dimensionality, it is necessary to use pre-analysis data reduction methods for many analyzes such as classification and regression analysis. In the solution of high-dimensionality, filter feature selection methods based on statistical criteria are widely used in terms of simplicity and efficiency. One of the important problems with filter feature selection methods is the selection of multiple features carrying the same information unnecessarily when strong correlations exist between features. In this study, a novel approach is proposed to solve this problem of filter feature selection methods. In addition, with the proposed new approach, the question of how many appropriate features will be included is also solved. The performance of the proposed approach is demonstrated on high-dimensional reflectance data with high correlations between features. The results obtained revealed that the proposed approach improves the classification performance of filter feature selection methods in mixture discriminant analysis in terms of classification accuracy and entropy criteria.
URI: https://doi.org/10.1109/ACCESS.2023.3325331
https://hdl.handle.net/11499/54973
ISSN: 2169-3536
Appears in Collections:Fen Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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