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| Unsupervised Attributes Selection Algorithm for Multivariate Time Series |
| WU Hu-Sheng1,2, ZHANG Feng-Ming1, XU Xian-Liang3, ZHANG Chao1, DU Ji-Yong1 |
1.Materiel Management and Safety Engineering College, Air Force Engineering University, Xian 710051
2.Materiel Engineering College, Armed Police Force Engineering University, Xian 710086
3.Military Deputy Office of PLA in Qingdao, Qingdao 266111 |
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Abstract Attribute selection is an effective data preprocessing method. It can keep temporal relations of important attributes of multivariate time series and their actual physical meanings. Aiming at the problem that the actual data lacks the classified information, an unsupervised attribute selection method is proposed and its time complexity is analyzed.Firstly,a method for computing the fractal dimension of multivariate time series is proposed, and there is no need for the proposed method to reconstruct the phase space. The fractal dimension is considered as the essential dimension by the proposed method. Therefore,the changing of the attributes number and the fractal dimension of attribute subsets are regarded as the evaluation criterion of attribute subsets. To solve the combinatorial explosion problem in high dimensional search space, the discrete particle swarm optimization algorithm is improved. Finally, the results of numerical simulations of multivariate time series from the typical chaotic dynamic system and five datasets of UCI database confirm the effectiveness of the proposed algorithm.Moreover, experimental results show the proposed algorithm finds out better attributes sets in shorter time and achieves better integrative performance.
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Received: 13 November 2012
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2013, Vol. 26 
