Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features
In recent years,the research on the classification of time series has attracted more and more attention.Advanced time series classification methods are usually based on great feature representations.Shapelet refers to the discriminative subsequences in time series,which can effectively express the l...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2022-07-01
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| Series: | Jisuanji kexue |
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| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-7-40.pdf |
| _version_ | 1797845167676325888 |
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| author | GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang |
| author_facet | GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang |
| author_sort | GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang |
| collection | DOAJ |
| description | In recent years,the research on the classification of time series has attracted more and more attention.Advanced time series classification methods are usually based on great feature representations.Shapelet refers to the discriminative subsequences in time series,which can effectively express the local shape characteristics of time series.However,the high computational cost greatly limits the practicability of the Shapelet-based time series classification methods.In addition,traditional Shapelet can only describe the overall shape characteristics of the subsequence under Euclidean distance metric,so it is easy to be disturbed by noise and is difficult to mine other types of discriminative information contained in the subsequence.To deal with the aforementioned problems,a new time series classification algorithm,named random Shapelet forest embedded with canonical time series features,is proposed in this paper.The proposed algorithm is based on the following three key strategies:1)randomly select Shapelet and limit the scope of Shapelet to improve efficiency;2)embed multiple canonical time series features in Shapelet to improve the adaptability of the algorithm to different classification problems and make up for the accuracy loss caused by the random selection of Shapelet;3)build a random forest classifier based on the new feature representations to ensure the generalization ability of the algorithm.Experimental results on 112 UCR time series datasets show that the proposed algorithm is more accurate than the STC algorithm which is based on Shapelet exact search and the Shapelet transform technique,as well as many other types of state-of-the-art time series classification algorithms.Moreover,extensive experimental comparisons verify the significant advantages of the proposed algorithm in terms of efficiency. |
| first_indexed | 2024-04-09T17:35:30Z |
| format | Article |
| id | doaj.art-efcb59bc9b754941ac80375747949f2b |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-04-09T17:35:30Z |
| publishDate | 2022-07-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-efcb59bc9b754941ac80375747949f2b2023-04-18T02:32:12ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2022-07-01497404910.11896/jsjkx.210700226Random Shapelet Forest Algorithm Embedded with Canonical Time Series FeaturesGAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang0School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China;Beijing Key Laboratory of Traffic Data Analysis and Mining,Beijing 100044,ChinaIn recent years,the research on the classification of time series has attracted more and more attention.Advanced time series classification methods are usually based on great feature representations.Shapelet refers to the discriminative subsequences in time series,which can effectively express the local shape characteristics of time series.However,the high computational cost greatly limits the practicability of the Shapelet-based time series classification methods.In addition,traditional Shapelet can only describe the overall shape characteristics of the subsequence under Euclidean distance metric,so it is easy to be disturbed by noise and is difficult to mine other types of discriminative information contained in the subsequence.To deal with the aforementioned problems,a new time series classification algorithm,named random Shapelet forest embedded with canonical time series features,is proposed in this paper.The proposed algorithm is based on the following three key strategies:1)randomly select Shapelet and limit the scope of Shapelet to improve efficiency;2)embed multiple canonical time series features in Shapelet to improve the adaptability of the algorithm to different classification problems and make up for the accuracy loss caused by the random selection of Shapelet;3)build a random forest classifier based on the new feature representations to ensure the generalization ability of the algorithm.Experimental results on 112 UCR time series datasets show that the proposed algorithm is more accurate than the STC algorithm which is based on Shapelet exact search and the Shapelet transform technique,as well as many other types of state-of-the-art time series classification algorithms.Moreover,extensive experimental comparisons verify the significant advantages of the proposed algorithm in terms of efficiency.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-7-40.pdftime series|classification|shapelet|canonical time series features|random forest |
| spellingShingle | GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features Jisuanji kexue time series|classification|shapelet|canonical time series features|random forest |
| title | Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features |
| title_full | Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features |
| title_fullStr | Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features |
| title_full_unstemmed | Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features |
| title_short | Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features |
| title_sort | random shapelet forest algorithm embedded with canonical time series features |
| topic | time series|classification|shapelet|canonical time series features|random forest |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-7-40.pdf |
| work_keys_str_mv | AT gaozhenzhuowangzhihailiuhaiyang randomshapeletforestalgorithmembeddedwithcanonicaltimeseriesfeatures |