期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 43, 期 -, 页码 237-249出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2015.08.055
关键词
Multivariate time series classification; Recurrent neural network; Adaptive differential evolution algorithm
类别
资金
- National Natural Science Foundation of China [71531009, 71371080]
- Humanities and Social Sciences Foundation of Chinese Ministry of Education [15YJA630095]
The multivariate time series (MIS) classification is a very difficult process because of the complexity of the MIS data type. Among all the methods to resolve this problem, the attribute-value representation classification approaches are the most popular. Despite their proven effectiveness of these however, these approaches are time consuming, sensitive to noise, or prone to damage of inner data properties as well as capable of producing undesirable accuracy. In this paper, we propose a new approach (CADS) for MIS classification that utilizes recurrent neural network (RNN) and adaptive differential evolution (ADE) algorithm. The approach can effectively overcome specific shortcomings of the attribute-value representation approaches. The principle of this approach adheres to three steps. First, an RNN is used to project the training MIS samples into different state clouds (samples in the same class are projected into a state cloud). Second, classifiers from these state clouds are induced for different classes. Third, the final MIS classifiers are obtained using ADE for parameter optimization. This approach makes full use of the network state space of a given RNN to induce classifiers rather than to train the network. Experimental results performed on 18 data sets demonstrate the accuracy and robustness of the proposed approach for MIS classification. As a new and universal approach, CADS can be very effective and stable for handling a variety of complex classification problems. (C) 2015 Elsevier Ltd. All rights reserved.
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