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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 12

Feature selection of time-series neighborhood based on dtwm metric

Yang Xuan1,2
Wang Xiaowan3
Hu Lingzhi1
Wu Di1
1. School of Basic Medical Science, Shaanxi University of Chinese Medicine, Xianyang Shaanxi 712046, China
2. School of Science, Chang'an University, Xi'an 710064, China
3. School of Software, Tsinghua University, Beijing 100084, China
DOI: 10.19734/j.issn.1001-3695.2025.05.0157

Abstract

High-dimensional time series data exist widely in real life. They often have decision attributes and vary in time length. These characteristics render existing neighborhood rough set feature selection algorithms inapplicable or reduce their classification performance. Thus, a feature selection method for high-dimensional time series data based on metrics is proposed. First, the Mahalanobis distance is introduced. It defines the Dynamic Time Warping of Mahalanobis (DTWM) to measure the similarity between attributes. Then, a time series decision information system is defined. It stores non-equal-length high-dimensional time series data. A time series neighborhood relationship and a time series neighborhood rough set model based on DTWMdistance measurement are also proposed. Finally, internal and external importance are defined. Attribute dependency serves as a key indicator for screening and selecting attributes. A feature selection method for high-dimensional time series data based onDTWMmeasurement is thereby put forward. Experiments on five public datasets verify the algorithm has an average improvement of 14.2% and 21.7% in classification accuracy. These results fully validate the effectiveness and superiority of the proposed method.

Foundation Support

国家优秀青年科学基金项目(61822111)
陕西省教育教学改革研究项目23ZY022、陕西中医药大学教育教学改革研究项目23jg01

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.05.0157
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 12

Publish History

[2025-08-20] Accepted Paper

Cite This Article

杨璇, 王潇婉, 胡灵芝, 等. 基于DTWM的时序邻域特征选择算法 [J]. 计算机应用研究, 2025, 42 (12). (2025-08-21). https://doi.org/10.19734/j.issn.1001-3695.2025.05.0157. (Yang Xuan, Wang Xiaowan, Hu Lingzhi, et al. Feature selection of time-series neighborhood based on dtwm metric [J]. Application Research of Computers, 2025, 42 (12). (2025-08-21). https://doi.org/10.19734/j.issn.1001-3695.2025.05.0157. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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