Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6711
Title: Feature Selection for Optimal Weather Detection with Meteorological Radar Data
Authors: Hamurcu, Eren
Yetik, İmam Şamil
Keywords: radar
meteorology
classification
feature selection
Issue Date: 2018
Publisher: IEEE
Source: 26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEY
Series/Report no.: Signal Processing and Communications Applications Conference
Abstract: In this paper, the data of Dual-Pole Meteorology Radar which is taken from the MGM and located in Hatay, is used for developed weather detection with feature selection. Classical classification methods were used first to weather detection. After the results of this classification, studies were conducted to select only the features that are critical to classification, rather than all features, with a view to reducing the level of performance with fewer features. These studies were conducted on two classifiers; a classifier was first used to detect "rainfall" or "no rainfall" in the region and then classified for "bird-insect" or "clutter". From the total of eight features found in our database, the most important and most useful features for both classifiers have been determined. The results show that the feature selection method we have developed has a similar performance when a few attributes are used instead of all. Thus, it is possible to achieve similar classification performance with lower calculation capacity.
URI: https://hdl.handle.net/20.500.11851/6711
ISBN: 978-1-5386-1501-0
ISSN: 2165-0608
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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