NDLI: Sets with incomplete and missing data — NN radar signal classification

Content Provider	IEEE Xplore Digital Library
Author	Jordanov, I. Petrov, N.
Copyright Year	2014
Description	Author affiliation: Sch. of Comput., Univ. of Portsmouth, Portsmouth, UK (Jordanov, I.; Petrov, N.)
Abstract	We investigate further the problem of radar signal classification and source identification with neural networks. The available large dataset includes pulse train characteristics such as signal frequencies, type of modulation, pulse repetition intervals, scanning type, scan period, etc., represented as a mixture of continuous, discrete and categorical data. Typically, considerable part of the data samples is with missing values. In our previous work we used only part of the radar dataset, applying listwise deletion to get rid of the samples with missing values and processed relatively small subset of complete data. In this work we apply multiple imputation (MI) method, which is a model based approach of dealing with missing data, by producing confidence intervals for unbiased estimates without loss of statistical power (using both complete and incomplete cases). We employ MI to all data samples with up to 60% missingness, this way increasing more than twice the size of the initially used data subset. We apply feedforward backpropagation neural network (NN) supervised learning for solving the classification and identification problem and investigate and critically compare the same three case studies, researched in the previous paper and report improved, superior results, which is a consequence of the implemented MI and improved NN training.
Starting Page	218
Ending Page	224
File Size	2955252
Page Count	7
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889852
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radar Artificial neural networks Manganese Encoding Radio frequency Training radar signal classification missing data multiple imputation neural networks supervised learning
Content Type	Text
Resource Type	Article

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