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A Novel Feature Selection Approach Based on FODPSO and SVM

A Novel Feature Selection Approach Based on FODPSO and SVM

Title: A Novel Feature Selection Approach Based on FODPSO and SVM
Author: Benediktsson, Jon Atli   orcid.org/0000-0003-0621-9647
Ghamisi, Pedram
Couceiro, Micael S.
Date: 2015-05
Language: English
Scope: 2935-2947
University/Institute: Háskóli Íslands
University of Iceland
School: Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Department: Rafmagns- og tölvuverkfræðideild (HÍ)
Faculty of Electrical and Computer Engineering (UI)
Series: IEEE Transactions on Geoscience and Remote Sensing;53(5)
ISSN: 0196-2892
1558-0644 (e-ISSN)
DOI: 10.1109/TGRS.2014.2367010
Subject: Support vector machines; Data reduction; Hyperspectral imaging
URI: https://hdl.handle.net/20.500.11815/139

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P. Ghamisi, M. S. Couceiro and J. A. Benediktsson, (2015) A Novel Feature Selection Approach Based on FODPSO and SVM, IEEE Transactions on Geoscience and Remote Sensing,53,(5), 2935-2947. doi: 10.1109/TGRS.2014.2367010


A novel feature selection approach is proposed to address the curse of dimensionality and reduce the redundancy of hyperspectral data. The proposed approach is based on a new binary optimization method inspired by fractional-order Darwinian particle swarm optimization (FODPSO). The overall accuracy (OA) of a support vector machine (SVM) classifier on validation samples is used as fitness values in order to evaluate the informativity of different groups of bands. In order to show the capability of the proposed method, two different applications are considered. In the first application, the proposed feature selection approach is directly carried out on the input hyperspectral data. The most informative bands selected from this step are classified by the SVM. In the second application, the main shortcoming of using attribute profiles (APs) for spectral-spatial classification is addressed. In this case, a stacked vector of the input data and an AP with all widely used attributes are created. Then, the proposed feature selection approach automatically chooses the most informative features from the stacked vector. Experimental results successfully confirm that the proposed feature selection technique works better in terms of classification accuracies and CPU processing time than other studied methods without requiring the number of desired features to be set a priori by users.


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