{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:10:55Z","timestamp":1776276655718,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773138"],"award-info":[{"award-number":["61773138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019536","name":"Science Fund for Distinguished Young Scholars of Heilongjiang Province","doi-asserted-by":"publisher","award":["LH2021F025"],"award-info":[{"award-number":["LH2021F025"]}],"id":[{"id":"10.13039\/501100019536","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s10489-022-04386-3","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T12:08:32Z","timestamp":1673525312000},"page":"17778-17795","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["FT-FVC: fast transformation-based feature vector concatenation for time series classification"],"prefix":"10.1007","volume":"53","author":[{"given":"Changchun","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4945-6271","authenticated-orcid":false,"given":"Xin","family":"Huo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hewei","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"issue":"3","key":"4386_CR1","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","volume":"31","author":"A Bagnall","year":"2017","unstructured":"Bagnall A, Lines J, Bostrom A, Large J, Keogh E (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Disc 31 (3):606\u2013660","journal-title":"Data Min Knowl Disc"},{"issue":"4","key":"4386_CR2","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"HI Fawaz","year":"2019","unstructured":"Fawaz HI, Forestier G, Weber J, Idoumghar L, Muller P-A (2019) Deep learning for time series classification: a review. Data Min Knowl Disc 33(4):917\u2013963","journal-title":"Data Min Knowl Disc"},{"issue":"5","key":"4386_CR3","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1007\/s10618-020-00701-z","volume":"34","author":"A Dempster","year":"2020","unstructured":"Dempster A, Petitjean F, Webb GI (2020) Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min Knowl Disc 34(5):1454\u20131495","journal-title":"Data Min Knowl Disc"},{"issue":"11","key":"4386_CR4","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1109\/TKDE.2005.186","volume":"17","author":"Z Zhi-Hua","year":"2005","unstructured":"Zhi-Hua Z, Ming L (2005) Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans Knowl Data Eng 17(11):1529\u20131541","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"4386_CR5","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s10618-019-00663-x","volume":"34","author":"CW Tan","year":"2020","unstructured":"Tan CW, Petitjean F, Webb GI (2020) Fastee: fast ensembles of elastic distances for time series classification. Data Min Knowl Disc 34(1):231\u2013272","journal-title":"Data Min Knowl Disc"},{"key":"4386_CR6","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.patcog.2018.04.003","volume":"81","author":"D Folgado","year":"2018","unstructured":"Folgado D, Barandas M, Matias R, Martins R, Carvalho M, Gamboa H (2018) Time alignment measurement for time series. Pattern Recogn 81:268\u2013279","journal-title":"Pattern Recogn"},{"key":"4386_CR7","doi-asserted-by":"publisher","first-page":"107210","DOI":"10.1016\/j.patcog.2020.107210","volume":"102","author":"H Deng","year":"2020","unstructured":"Deng H, Chen W, Shen Q, Ma AJ, Yuen PC, Feng G (2020) Invariant subspace learning for time series data based on dynamic time warping distance. Pattern Recogn 102:107210","journal-title":"Pattern Recogn"},{"issue":"2","key":"4386_CR8","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1007\/s10618-012-0251-4","volume":"26","author":"G Tomasz","year":"2013","unstructured":"Tomasz G, Luczak M (2013) Using derivatives in time series classification. Data Min Knowl Disc 26(2):310\u2013331","journal-title":"Data Min Knowl Disc"},{"key":"4386_CR9","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.knosys.2014.02.011","volume":"61","author":"G Tomasz","year":"2014","unstructured":"Tomasz G, M L (2014) Non-isometric transforms in time series classification using dtw. Knowledge-based systems 61:98\u2013108","journal-title":"Knowledge-based systems"},{"issue":"4","key":"4386_CR10","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1007\/s10618-013-0322-1","volume":"28","author":"J Hills","year":"2014","unstructured":"Hills J, Lines J, Baranauskas E, Mapp J, Bagnall A (2014) Classification of time series by shapelet transformation. Data Min Knowl Disc 28(4):851\u2013881","journal-title":"Data Min Knowl Disc"},{"issue":"2","key":"4386_CR11","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/s10489-019-01535-z","volume":"50","author":"Q Yan","year":"2020","unstructured":"Yan Q, Cao Y (2020) Optimizing shapelets quality measure for imbalanced time series classification. Appl Intell 50(2):519\u2013536","journal-title":"Appl Intell"},{"key":"4386_CR12","doi-asserted-by":"crossref","unstructured":"Chen J, Wan Y, Wang X, Xuan Y (2022) Learning-based shapelets discovery by feature selection for time series classification, Applied Intelligence, pp 1\u201316","DOI":"10.1007\/s10489-022-04422-2"},{"issue":"6","key":"4386_CR13","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1007\/s10618-014-0377-7","volume":"29","author":"P Schafer","year":"2015","unstructured":"Schafer P (2015) The boss is concerned with time series classification in the presence of noise. Data Min Knowl Disc 29(6):1505\u20131530","journal-title":"Data Min Knowl Disc"},{"key":"4386_CR14","doi-asserted-by":"crossref","unstructured":"Middlehurst M, Vickers W, Bagnall A (2019) Scalable dictionary classifiers for time series classification. In: International conference on intelligent data engineering and automated learning, pp 11\u201319","DOI":"10.1007\/978-3-030-33607-3_2"},{"key":"4386_CR15","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.ins.2013.02.030","volume":"239","author":"H Deng","year":"2013","unstructured":"Deng H, Runger G, Tuv E, Vladimir M (2013) A time series forest for classification and feature extraction. Inf Sci 239:142\u2013153","journal-title":"Inf Sci"},{"key":"4386_CR16","doi-asserted-by":"crossref","unstructured":"Cabello N, Naghizade E, Qi J, Kulik L (2020) Fast and accurate time series classification through supervised interval search. In: 2020 IEEE International conference on data mining, pp 948\u2013953","DOI":"10.1109\/ICDM50108.2020.00107"},{"key":"4386_CR17","doi-asserted-by":"crossref","unstructured":"Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International joint conference on neural networks, pp 1578\u20131585","DOI":"10.1109\/IJCNN.2017.7966039"},{"issue":"6","key":"4386_CR18","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","volume":"34","author":"HI Fawaz","year":"2020","unstructured":"Fawaz HI, Lucas B, Forestier G, Pelletier C, Petitjean F (2020) Inceptiontime: finding alexnet for time series classification. Data Min Knowl Disc 34(6):1936\u20131962","journal-title":"Data Min Knowl Disc"},{"issue":"3","key":"4386_CR19","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1007\/s10489-019-01552-y","volume":"50","author":"A Gautam","year":"2020","unstructured":"Gautam A, Singh V (2020) Clr-based deep convolutional spiking neural network with validation based stopping for time series classification. Appl Intell 50(3):830\u2013848","journal-title":"Appl Intell"},{"key":"4386_CR20","doi-asserted-by":"crossref","unstructured":"Dempster A, Petitjean F, Webb GI (2021) Minirocket: a very fast (almost) deterministic transform for time series classification. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery data mining, pp 248\u2013257","DOI":"10.1145\/3447548.3467231"},{"issue":"5","key":"4386_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3182382","volume":"12","author":"J Lines","year":"2018","unstructured":"Lines J, Taylor S, Bagnall A (2018) Time series classification with hive-cote: the hierarchical vote collective of transformation-based ensembles. ACM transactions on knowledge discovery from data 12(5):1\u201335","journal-title":"ACM transactions on knowledge discovery from data"},{"key":"4386_CR22","doi-asserted-by":"crossref","unstructured":"Bagnall A, Flynn M, Large J, Lines J, Middlehurst M (2020) On the usage and performance of the hierarchical vote collective of transformation-based ensembles version 1.0 (hive-cote v1.0). In: Advanced analytics and learning on temporal data, pp 3\u201318","DOI":"10.1007\/978-3-030-65742-0_1"},{"issue":"11-12","key":"4386_CR23","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.1007\/s10994-021-06057-9","volume":"110","author":"M Middlehurst","year":"2021","unstructured":"Middlehurst M, Large J, Flynn M, Lines J, Bostrom A, Bagnall A (2021) Hive-cote 2.0: a new meta ensemble for time series classification. Mach Learn 110(11-12):3211\u20133243","journal-title":"Mach Learn"},{"issue":"3","key":"4386_CR24","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1007\/s10618-020-00679-8","volume":"34","author":"A Shifaz","year":"2020","unstructured":"Shifaz A, Pelletier C, Petitjean F, Webb GI (2020) Ts-chief: a scalable and accurate forest algorithm for time series classification. Data Min Knowl Disc 34(3):742\u2013775","journal-title":"Data Min Knowl Disc"},{"issue":"4","key":"4386_CR25","doi-asserted-by":"publisher","first-page":"2168","DOI":"10.1109\/TPAMI.2020.3031898","volume":"44","author":"G Qi","year":"2022","unstructured":"Qi G, Luo J (2022) Small data challenges in big data era: a survey of recent progress on unsupervised and semi-supervised methods. IEEE Trans Pattern Anal Mach Intell 44(4):2168\u20132187","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"4386_CR26","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","volume":"109","author":"V Engelen","year":"2020","unstructured":"Engelen V, Jesper E, Hoos HH (2020) A survey on semi-supervised learning. Mach Learn 109(2):373\u2013440","journal-title":"Mach Learn"},{"key":"4386_CR27","doi-asserted-by":"crossref","unstructured":"Chen Y, Hu B, Keogh E, Batista GE (2013) Dtw-d: time series semi-supervised learning from a single example. In: Proceedings of the international conference on knowledge discovery and data mining, pp 383\u2013391","DOI":"10.1145\/2487575.2487633"},{"key":"4386_CR28","doi-asserted-by":"crossref","unstructured":"Xu Z, Funaya K (2015) Time series analysis with graph-based semi-supervised learning. In: Proceedings of the international conference on data science and advanced analytics, pp 1100\u20131105","DOI":"10.1109\/DSAA.2015.7344902"},{"issue":"2","key":"4386_CR29","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/s10115-017-1090-9","volume":"55","author":"M Gonz\u00e1lez","year":"2018","unstructured":"Gonz\u00e1lez M, Bergmeir C, Triguero I, Rodr\u00edguez Y, Ben\u00edtez JM (2018) Self-labeling techniques for semi-supervised time series classification: an empirical study. Knowl Inf Syst 55(2):493\u2013528","journal-title":"Knowl Inf Syst"},{"key":"4386_CR30","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.patcog.2018.02.030","volume":"80","author":"L Pagliosa","year":"2018","unstructured":"Pagliosa L, de Mello R (2018) Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis. Pattern Recogn 80:53\u201363","journal-title":"Pattern Recogn"},{"key":"4386_CR31","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.patcog.2018.12.026","volume":"89","author":"H Wang","year":"2019","unstructured":"Wang H, Zhang Q, Wu J, Pan S, Chen Y (2019) Time series feature learning with labeled and unlabeled data. Pattern Recogn 89:55\u201366","journal-title":"Pattern Recogn"},{"key":"4386_CR32","doi-asserted-by":"crossref","unstructured":"Xing H, Xiao Z, Zhan D, Luo S, Dai P, Li K (2022) Selfmatch: robust semisupervised time-series classification with self-distillation. International Journal of Intelligent Systems","DOI":"10.1002\/int.22957"},{"issue":"6","key":"4386_CR33","first-page":"2266","volume":"19","author":"V Gupta","year":"2019","unstructured":"Gupta V, Chopda MD, Pachori RB (2019) Cross-subject emotion recognition using flexible analytic wavelet transform from eeg signals. IEEE Transactions on Systems 19(6):2266\u2013 2274","journal-title":"IEEE Transactions on Systems"},{"issue":"12","key":"4386_CR34","first-page":"7382","volume":"51","author":"S Issa","year":"2021","unstructured":"Issa S, Peng Q, You X (2021) Emotion classification using eeg brain signals and the broad learning system. IEEE Transactions on Systems 51(12):7382\u20137391","journal-title":"IEEE Transactions on Systems"},{"key":"4386_CR35","doi-asserted-by":"crossref","unstructured":"Vidya B, P S (2022) Wearable multi-sensor data fusion approach for human activity recognition using machine learning algorithms. Sensors and Actuators: A Physical, vol 341","DOI":"10.1016\/j.sna.2022.113557"},{"issue":"6","key":"4386_CR36","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1109\/JAS.2019.1911747","volume":"6","author":"HA Dau","year":"2019","unstructured":"Dau HA, Bagnall A, Kamgar K, Yeh C-CM, Zhu Y, Gharghabi S, Ratanamahatana CA, Keogh E (2019) The ucr time series archive. IEEE\/CAA Journal of Automatica Sinica 6(6):1293\u2013 1305","journal-title":"IEEE\/CAA Journal of Automatica Sinica"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04386-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04386-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04386-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T12:14:23Z","timestamp":1688472863000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04386-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,12]]},"references-count":36,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["4386"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04386-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,12]]},"assertion":[{"value":"4 December 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}