An improved algorithm of compressing redundant data

Zhu, Yun; Hao, Wenning; Chen, Gang; Zhao, Siwei; Jin, Dawei

doi:10.1109/iceceng.2011.6057890

Cited by 1 publication

(2 citation statements)

References 3 publications

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“…Zhu et al [8] proposed a comparison clustering compressing redundant algorithm based on time-series to dynamically compress and clean redundant data in the data collection phase. Amin et al [9] proposed a multi-channels electroencephalogram signals data redundancy classification algorithm, which leverages SVM, multi-layer perceptron and k-nearest neighbor classifier to distinguish redundancy data.…”

Section: A Data Preprocessingmentioning

confidence: 99%

“…In this paper, SPA analyzes the traffic speeds of each hour independently. It calculates the variance value of traffic speeds within each hour by the Equation (8). If the variance value is less than the threshold, it means the data gathers together, and linear regression model can be used to build a curve and predict the traffic speed.…”

Section: ) Data Processing For the Time Segments With Violent Speed mentioning

confidence: 99%

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Redundancy-Reducing and Holiday Speed Prediction Based on Highway Traffic Speed Data

et al. 2019

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The accurate prediction of the highway traffic speed plays an important role in improving the production efficiency and the convenience of lives, and it is also one of the important issues in vehicle social big data analyses. Aiming at the problem of highway traffic speed data preprocessing, this paper proposes the algorithm of Redundant Data Reducing (RDR) that can greatly reduce the amount of data in model training of long short-term memory (LSTM) and improve the training speed under the condition that the influence on prediction accuracy is small and controllable. Aiming at the problem of low prediction accuracy for holiday traffic speeds due to small data volume, this paper proposes the Segment Prediction Algorithm (SPA) based on the speed features, which can effectively improve the prediction accuracy. The experimental results show that RDR can reduce the training data by up to 60% without notably affecting the prediction accuracy, while it improves the training speed by 60%. Compared with the current LSTM algorithm, the prediction accuracy of SPA improves significantly. INDEX TERMS Traffic speed data, redundancy reducing, holiday speed prediction, prediction accuracy.

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Section: A Data Preprocessingmentioning

confidence: 99%

Section: ) Data Processing For the Time Segments With Violent Speed mentioning

confidence: 99%