Application of Data Denoising and Classification Algorithm Based on RPCA and Multigroup Random Walk Random Forest in Engineering

Abstract

Data classification algorithms are often used in the engineering field, but the data measured in the actual engineering often contains different types and degrees of noise, such as vibration noise caused by water flow when measuring the natural frequencies of aqueducts or other hydraulic structures, which will affect the accuracy of classification. In reality, these noises often appear disorganized and stochastic and some existing algorithms exhibit poor performance in the face of these non-Gaussian noise. Therefore, the classification algorithms with excellent performance are needed. To address this issue, a hybrid algorithm of robust principal component analysis (RPCA) combined multigroup random walk random forest (MRWRF) is proposed in this paper. On the one hand RPCA can effectively remove part of non-Gaussian noise, and on the other hand MRWRF can select a better number of decision trees (DTs), which can effectively improve random forest (RF) robustness and classification performance, and the combination of RPCA and MRWRF can effectively classify data with non-Gaussian distribution noise. Compared with other existing algorithms, this hybrid algorithm has strong robustness and preferable classification performance and can thus provide a new approach for data classification problems in engineering.