Advanced SearchSearch Tips
Naive Bayes Classifier based Anomalous Propagation Echo Identification using Class Imbalanced Data
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Naive Bayes Classifier based Anomalous Propagation Echo Identification using Class Imbalanced Data
Lee, Hansoo; Kim, Sungshin;
  PDF(new window)
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.
Radar Data Analysis;Anomalous Propagation Echo;Naive Bayes Classifier;Class Imbalanced Data;SMOTE;
 Cited by
M. Grecu and W. F. Krajewski, "An efficient methodology for detection of anomalous propagation echoes in radar reflectivity data using neural networks," Journal of atmospheric and oceanic technology, vol. 17, no. 2, pp. 121-129, Feb. 2000. crossref(new window)

Y. Sun, A. K. Wong, and M. S. Kamel, "Classification of imbalanced data: a review," International journal of pattern recognition and artificial intelligence, vol. 23, no. 04, pp. 687-719, Jun. 2009. crossref(new window)

K. P. Murphy, Machine learning: a probabilistic perspective, Cambridge, MIT press, 2012.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, vol. 16, pp. 321-357, Jun. 2002.

R. J. Doviak and D. S. Zrnic, Doppler Radar & Weather Observations, Cambridge, Academic press, 2014.

G. Brussaard and P. A. Watson, Atmospheric modelling and millimetre wave propagation, New York, Springer Science & Business Media, 1995.

S. Moszkowicz, G. J. Ciach and W. F. Krajewski, "Statistical detection of anomalous propagation in radar reflectivity patterns," Journal of atmospheric and oceanic technology, vol. 11, no. 4, pp. 1026-1034, Aug. 1994. crossref(new window)

D. Heckerman, "Bayesian networks for data mining," Data mining and knowledge discovery, vol. 1, no. 1, pp. 79-119, Mar. 1997. crossref(new window)

R. E. Neapolitan, Learning bayesian networks, New Jersey, Pearson Prentice Hall, 2004.

A. McCallum and K. Nigam, "A comparison of event models for naive bayes text classification," in AAAI-98 workshop on learning for text categorization, vol. 752, Jul. 1998.

R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Machine learning: an artificial intelligence approach, New York, Springer Science & Business Media, 2013.