An Algorithm Based on KNN and Multiple Regression for the Missing-value Estimation of Sensors
李东芳1, 关伟2
1. Beijing Municipal Bridge Maintenance Management Group Co. Ltd., Beijing 100071, China; 2. Fundamental Research Innovation Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China
An Algorithm Based on KNN and Multiple Regression for the Missing-value Estimation of Sensors
LI Dong-fang1, GUAN Wei2
1. Beijing Municipal Bridge Maintenance Management Group Co. Ltd., Beijing 100071, China; 2. Fundamental Research Innovation Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China
摘要Missing sensor data are unavoidable when sensors are used to monitor a system. These missing data largely affect the sensor applications. When missing data exist, the best method is estimation. Herein, we introduce the k-nearest neighbor on multiple-regression algorithm (KMRA), which builds on the KNN and multiple regression. In the process of estimation, KMRA considers both spatial correlations from its neighbor sensor and time correlations from its own time serials. After computing these two correlations, the algorithm combines them into a unified result of estimation. As KMRA involves spatial and time correlations, it has the efficiency and practicability as an algorithm. Examination results show that KMRA can precisely estimate the missing data.
Abstract：Missing sensor data are unavoidable when sensors are used to monitor a system. These missing data largely affect the sensor applications. When missing data exist, the best method is estimation. Herein, we introduce the k-nearest neighbor on multiple-regression algorithm (KMRA), which builds on the KNN and multiple regression. In the process of estimation, KMRA considers both spatial correlations from its neighbor sensor and time correlations from its own time serials. After computing these two correlations, the algorithm combines them into a unified result of estimation. As KMRA involves spatial and time correlations, it has the efficiency and practicability as an algorithm. Examination results show that KMRA can precisely estimate the missing data.
李东芳, 关伟. An Algorithm Based on KNN and Multiple Regression for the Missing-value Estimation of Sensors[J]. Journal of Highway and Transportation Research and Development, 2020, 14(2): 7-15.
LI Dong-fang, GUAN Wei. An Algorithm Based on KNN and Multiple Regression for the Missing-value Estimation of Sensors. Journal of Highway and Transportation Research and Development, 2020, 14(2): 7-15.
[1] AO Dao-zhao, LI Guo-wei, LI Lin-sheng, et al. Auto-monitoring System of Expressway Slope Based on Sensor and Wireless Modes[J]. Journal of Highway and Transportation Research and Development, 2015, 32(11):41-47. (in Chinese) [2] ZHANG Bei-yang, ZHANG Xie-dong, CHEN Wei-dong, et al. Sensor Location Optimization of Large Span Bridge Based on Nested-stacking Genetic Algorithm[J]. Journal of Wuhan University of Technology:Transportation Science & Engineering Edition, 2016, 40(4):745-749. (in Chinese) [3] YI C, KIM L P. An Accurate and Robust Missing Value Estimation for Microarray Data:Least Absolute Deviation Imputation[C]//20065th International Conference on Machine Learning and Applications. Orlando, USA:IEEE, 2006:1-5. [4] BATISTA G E A P A, MONARD M C. An Analysis of Four Missing Data Treatment Methods for Supervised Learning[J]. Applied Artificial Intelligence, 2003, 17(5/6):519-533. [5] LIU C C, DAI D Q, YAN H. The Theoretic Framework of Local Weighted Approximation for Microarray Missing Value Estimation[J]. Pattern Recognition, 2010, 43(8):2993-3002. [6] ZHANG R, XU Z B, HUANG G B, et a1. Global Convergence of Online BP Training with Dynamic Learning Rate[J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(2):330-341. [7] PAN L, GAO H, LIU Y. A Spatial Correlation Based Adaptive Missing Data Estimation Algorithm in Wireless Sensor Networks[J]. International Journal of Wireless Information Networks, 2014, 21(4):280-289. [8] PAN L Q, LI J Z, LUO J Z. A Temporal and Spatial Correlation Based Missing Values Imputation Algorithm in Wireless Sensor Networks[J]. Chinese Journal of Computers, 2010, 33(1):1-11. [9] PAN Li-qiang, LI Jian-zhong. A Multiple-regression-model-based Missing Values Imputation Algorithm in Wireless Sensor Network[J]. Journal of Computer Research and Development, 2009, 46(12):2101-2110. (in Chinese) [10] XU Ke, LEI Jian-jun. Estimating Algorithm for Missing Values Based on Attribute Correlation in Wireless Sensor Network[J]. Journal of Computer Applications, 2015, 35(12):3341-3343, 3347. (in Chinese) [11] YUAN Yuan, SHAO Chun-fu, LIN Qiu-ying, et al. Repair of Traffic Flow Data Based on RBF Neural Network[J]. Transport Research, 2016, 2(5):46-52. (in Chinese) [12] LEE B, KIM K, CHUNG E Y. Replacement Policy Adaptable Miss Curve Estimation for Efficient Cache Partitioning[J]. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, 2017, 37(2):445-457. [13] ASIF M T, MITROVIC N, DAUWELS J, et al. Matrix and Tensor Based Methods for Missing Data Estimation in Large Traffic Networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(7):1816-1825. [14] CHEN Guang-ping. Missing Value Estimating Algorithm Based on Time Series Data Properties[J]. Computer Engineering and Applications, 2012, 48(12):135-138. (in Chinese) [15] XU Ke, LEI Jian-jun. Estimating Algorithm for Missing Values Based on Attribute Correlation in Wireless Sensor Network[J]. Journal of Computer Applications, 2015, 35(12):3341-3343. (in Chinese) [16] LI Shan, YU Ying, HU Kang-hua, et al. Missing Value Estimating Algorithm Based on Cloud Manufacturing Services QoS Time Series Data Properties[J]. Computer Integrated Manufacturing Systems, 2016, 22(12):2930-2936. (in Chinese) [17] LIU Zhao, DU Wei, YAN Dong-mei. Short-term Traffic Flow Forecast Based on Combination of K Nearest Neighbor Algorithm and Support Vector Regression[J]. Journal of Highway and Transportation Research and Development, 2017, 34(5):122-128. (in Chinese) [18] CHEN Fei-yan, TIAN Yu-chi, HU Liang. STUDY on KNN and BP Neural Network-based Prediction Model in IOT[J]. Computer Applications and Software, 2015, 32(6):127-130. (in Chinese)
2020, Vol. 14 
