{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:08:56Z","timestamp":1767337736296,"version":"3.41.0"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T00:00:00Z","timestamp":1586390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board","doi-asserted-by":"publisher","award":["ECR\/2016\/000406, ECR\/2017\/002419"],"award-info":[{"award-number":["ECR\/2016\/000406, ECR\/2017\/002419"]}],"id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Things"],"published-print":{"date-parts":[[2020,5,31]]},"abstract":"<jats:p>In the era of the Internet of Things (IoT), the sensor-based devices produce the Multivariate Time Series (MTS). A classification approach helps to predict the class label of an incoming MTS. Due to the large dimension and different sampling rate of the sensors in a given MTS, a classifier takes time to predict the class label. Some IoT applications may require early prediction of the class label where the classifier starts the prediction once the minimum number of data points are collected. In this article, we address the problem of early prediction of the class label of an MTS in IoT. This work considers the sensors with different sampling rate to generate the MTS. Each sensor generates a time series (component) of the MTS. We propose a Divide-and-Conquer\u2013based early classification approach for classifying such MTS. The approach constructs an ensemble classifier using a probabilistic classifier and hierarchical clustering. The ensemble classifier employs a Divide-and-Conquer method to handle the different sampling rate components during the prediction of class label. The experimental results show that our approach significantly outperforms the existing approaches on real-world datasets using various evaluation metrics.<\/jats:p>","DOI":"10.1145\/3375877","type":"journal-article","created":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T11:35:06Z","timestamp":1586432106000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["A Divide-and-Conquer\u2013based Early Classification Approach for Multivariate Time Series with Different Sampling Rate Components in IoT"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1424-1361","authenticated-orcid":false,"given":"Ashish","family":"Gupta","sequence":"first","affiliation":[{"name":"Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India"}]},{"given":"Hari Prabhat","family":"Gupta","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India"}]},{"given":"Bhaskar","family":"Biswas","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India"}]},{"given":"Tanima","family":"Dutta","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India"}]}],"member":"320","published-online":{"date-parts":[[2020,4,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-016-0483-9"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2850347"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/1248547.1248548"},{"key":"e_1_2_1_4_1","unstructured":"Dua Dheeru and Efi Karra Taniskidou. 2019. UCI Machine Learning Repository. Retrieved from http:\/\/archive.ics.uci.edu\/ml.  Dua Dheeru and Efi Karra Taniskidou. 2019. UCI Machine Learning Repository. Retrieved from http:\/\/archive.ics.uci.edu\/ml."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017197"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.121"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2316504"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.07.056"},{"volume-title":"Proceedings of the 22nd ACM International Conference on Information 8 Knowledge Management. 1889--1892","author":"He G.","key":"e_1_2_1_9_1","unstructured":"G. He , Y. Duan , T. Qian , and X. Chen . 2013. Early prediction on imbalanced multivariate time series . In Proceedings of the 22nd ACM International Conference on Information 8 Knowledge Management. 1889--1892 . G. He, Y. Duan, T. Qian, and X. Chen. 2013. Early prediction on imbalanced multivariate time series. In Proceedings of the 22nd ACM International Conference on Information 8 Knowledge Management. 1889--1892."},{"volume-title":"Proceedings of IEEE 5th World Forum on Internet of Things (WF-IoT\u201919)","author":"Gupta A.","key":"e_1_2_1_10_1","unstructured":"A. Gupta , R. Pal , R. Mishra , H. P. Gupta , T. Dutta , and P. Hirani . 2019. Game theory based early classification of rivers using time series data . In Proceedings of IEEE 5th World Forum on Internet of Things (WF-IoT\u201919) . 686--691. A. Gupta, R. Pal, R. Mishra, H. P. Gupta, T. Dutta, and P. Hirani. 2019. Game theory based early classification of rivers using time series data. In Proceedings of IEEE 5th World Forum on Internet of Things (WF-IoT\u201919). 686--691."},{"volume-title":"Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. 541--553","author":"Hsu En-Yu","key":"e_1_2_1_11_1","unstructured":"En-Yu Hsu , Chien-Liang Liu , and Vincent S. Tseng . 2019. Multivariate time series early classification with interpretability using deep learning and attention mechanism . In Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. 541--553 . En-Yu Hsu, Chien-Liang Liu, and Vincent S. Tseng. 2019. Multivariate time series early classification with interpretability using deep learning and attention mechanism. In Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. 541--553."},{"volume-title":"Proceedings of IEEE 14th International Conference on Data Mining. 310--319","author":"Li K.","key":"e_1_2_1_12_1","unstructured":"K. Li , S. Li , and Y. Fu . 2014. Early classification of ongoing observation . In Proceedings of IEEE 14th International Conference on Data Mining. 310--319 . K. Li, S. Li, and Y. Fu. 2014. Early classification of ongoing observation. In Proceedings of IEEE 14th International Conference on Data Mining. 310--319."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2018.2832664"},{"volume-title":"Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. 199--211","author":"Lin Y. F.","key":"e_1_2_1_14_1","unstructured":"Y. F. Lin , H. H. Chen , V. S. Tseng , and J. Pei . 2015. Early classification on multivariate time series with numerical and categorical attributes . In Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. 199--211 . Y. F. Lin, H. H. Chen, V. S. Tseng, and J. Pei. 2015. Early classification on multivariate time series with numerical and categorical attributes. In Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. 199--211."},{"key":"e_1_2_1_15_1","article-title":"Smart meter data analytics: Systems, algorithms, and benchmarking","volume":"42","author":"Liu Xiufeng","year":"2016","unstructured":"Xiufeng Liu , Lukasz Golab , Wojciech Golab , Ihab F. Ilyas , and Shichao Jin . 2016 . Smart meter data analytics: Systems, algorithms, and benchmarking . ACM Trans. Database Syst. 42 , 1 (2016), 2:1\u20132:39. Xiufeng Liu, Lukasz Golab, Wojciech Golab, Ihab F. Ilyas, and Shichao Jin. 2016. Smart meter data analytics: Systems, algorithms, and benchmarking. ACM Trans. Database Syst. 42, 1 (2016), 2:1\u20132:39.","journal-title":"ACM Trans. Database Syst."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-13-195"},{"volume-title":"Proceedings of IEEE 13th International Conference on Data Mining. 201--210","author":"Ghalwash M. F.","key":"e_1_2_1_17_1","unstructured":"M. F. Ghalwash , V. Radosavljevic , and Z. Obradovic . 2013. Extraction of interpretable multivariate patterns for early diagnostics . In Proceedings of IEEE 13th International Conference on Data Mining. 201--210 . M. F. Ghalwash, V. Radosavljevic, and Z. Obradovic. 2013. Extraction of interpretable multivariate patterns for early diagnostics. In Proceedings of IEEE 13th International Conference on Data Mining. 201--210."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2651018"},{"key":"e_1_2_1_20_1","doi-asserted-by":"crossref","unstructured":"C. E. Rasmussen and C. Williams. 2006. Gaussian Processes for Machine Learning. MIT Press Cambridge MA.  C. E. Rasmussen and C. Williams. 2006. Gaussian Processes for Machine Learning. MIT Press Cambridge MA.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131344"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2670022"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-016-0462-1"},{"key":"e_1_2_1_24_1","first-page":"1","article-title":"Early classification of time series by simultaneously optimizing the accuracy and earliness","volume":"29","author":"Mori U.","year":"2017","unstructured":"U. Mori , A. Mendiburu , S. Dasgupta , and J. A. Lozano . 2017 . Early classification of time series by simultaneously optimizing the accuracy and earliness . IEEE Trans. Neural Netw. Learn. Syst. 29 , 10 (2017), 1 -- 10 . U. Mori, A. Mendiburu, S. Dasgupta, and J. A. Lozano. 2017. Early classification of time series by simultaneously optimizing the accuracy and earliness. IEEE Trans. Neural Netw. Learn. Syst. 29, 10 (2017), 1--10.","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.2307\/3001968"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2014.2307795"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0400-x"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2014.2306328"}],"container-title":["ACM Transactions on Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3375877","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3375877","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:38:15Z","timestamp":1750199895000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3375877"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,9]]},"references-count":28,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,5,31]]}},"alternative-id":["10.1145\/3375877"],"URL":"https:\/\/doi.org\/10.1145\/3375877","relation":{},"ISSN":["2577-6207"],"issn-type":[{"type":"electronic","value":"2577-6207"}],"subject":[],"published":{"date-parts":[[2020,4,9]]},"assertion":[{"value":"2018-11-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-12-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-04-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}