Two-stage Unsupervised Feature Selection Method Oriented to Manufacturing Procedural Data

doi:10.3901/JME.2019.17.133

Abstract

Abstract: In a modernized manufacturing workshop, myriads of data are incessantly produced and a large part of those are stored in the industrial big data platform of the modern manufacturing enterprise in the form of structuralized unlabeled raw data. Those manufacturing data are of great latent exploitative value, because of their characteristics of high-noise and high-redundancy, however, direct analysis and utilization of them are impossible. Aiming at reducing the redundancy of manufacturing procedural data and excavating their local structure, a two-stage unsupervised feature selection method is proposed. In the first stage of the method, subset of the original feature set generated by genetic algorithm(GA) is utilized as the input features of radius basis function neural network(RBFNN), to reconstruct the unabridged original feature set. The ratio of dimensionality reduction and reconstructional accuracy are calculated jointly as the fitness function of GA, which is optimized by iteration to learn a low-dimensional representation of high-dimensional features, removing redundant and noisy features of the origin feature set. In the second stage, Laplacian score(LS) is employed to evaluate the locality preserving power of the remaining features, unearthing features which are prone to improving the performance of classification. By comparing with other unsupervised feature selection method, the method proposed here is proven more effective in reducing the redundancy of manufacturing data and simultaneously enhancing the performance of classification.

Key words: unsupervised feature selection, genetic algorithm, radius basis function neural network, Laplacian score, manufacturing procedural data

CLC Number:

TG156

ZHANG Jie, SHENG Xia, ZHANG Peng, QIN Wei, ZHAO Xinming. Two-stage Unsupervised Feature Selection Method Oriented to Manufacturing Procedural Data[J]. Journal of Mechanical Engineering, 2019, 55(17): 133-144.

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