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http://dx.doi.org/10.6109/jkiice.2016.20.6.1063

Naive Bayes Classifier based Anomalous Propagation Echo Identification using Class Imbalanced Data  

Lee, Hansoo (Department of Electrical and Computer Engineering, Pusan National University)
Kim, Sungshin (Department of Electrical and Computer Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.20, no.6, 2016 , pp. 1063-1068 More about this Journal
Abstract
Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar due to its observation principle and disturb weather forecasting process. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo with data mining techniques. This paper conducts researches about implementation of classification method which can separate the anomalous propagation echo in the raw radar data using naive Bayes classifier with various kinds of observation results. Considering that collected data has a class imbalanced problem, this paper includes SMOTE method. It is confirmed that the fine classification results are derived by the suggested classifier with balanced dataset using actual appearance cases of the echo.
Keywords
Radar Data Analysis; Anomalous Propagation Echo; Naive Bayes Classifier; Class Imbalanced Data; SMOTE;
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