A Data Change Rule Based Empirical Framework for Labeling Unlabeled Time Series Driving Data
Keyword(s):
Most of the driving maneuver classification methods<br>follow supervised learning techniques and utilize ground truth in order to train classifiers. However, collecting ground truth is the most troublesome, expensive, and significant task of classification and effects a classifier’s performance. The work proposes an empirical framework for automatic labeling of timeseries data that can be further used in training phrases during semi-supervised learning. The proposed algorithm generates class labels and find that generated label of 4895 data matched with 11077 manual labeled data. The work analyzes the challenges involved in the driving time series data labeling. So, reasons behind mismatch of data label can also be explained.
2020 ◽
Keyword(s):
2020 ◽
pp. 1-14
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2: EARLY PREDICTION OF PATIENT DETERIORATION USING MACHINE LEARNING TECHNIQUES WITH TIME SERIES DATA
2016 ◽
Vol 44
(12)
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pp. 87-87
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1996 ◽
Vol 19
(5)
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pp. 302-306
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2019 ◽
pp. 269-282
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Keyword(s):
A Performance Comparison of Statistical and Machine Learning Techniques in Learning Time Series Data
2015 ◽
Vol 21
(10)
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pp. 3037-3041
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