{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:52:18Z","timestamp":1743007938763,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030880804"},{"type":"electronic","value":"9783030880811"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-88081-1_36","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T11:07:08Z","timestamp":1632913628000},"page":"480-493","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Decision Combination in Classifier Committee Built on Deep Embedding Features"],"prefix":"10.1007","author":[{"given":"Jacek","family":"Treli\u0144ski","sequence":"first","affiliation":[]},{"given":"Bogdan","family":"Kwolek","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"issue":"3","key":"36_CR1","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","volume":"6","author":"R Polikar","year":"2006","unstructured":"Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21\u201345 (2006)","journal-title":"IEEE Circuits Syst. Mag."},{"issue":"2","key":"36_CR2","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1023\/A:1022859003006","volume":"51","author":"LI Kuncheva","year":"2003","unstructured":"Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181\u2013207 (2003)","journal-title":"Mach. Learn."},{"issue":"3","key":"36_CR3","first-page":"418","volume":"22","author":"L Xu","year":"1992","unstructured":"Xu, L., Krzyzak, A., Suen, C.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. SMC 22(3), 418\u2013435 (1992)","journal-title":"IEEE Trans. SMC"},{"issue":"2","key":"36_CR4","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/34.982906","volume":"24","author":"LI Kuncheva","year":"2002","unstructured":"Kuncheva, L.I.: A theoretical study on six classifier fusion strategies. IEEE Trans. Pattern Anal. Mach. Intell. 24(2), 281\u2013286 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"36_CR5","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1109\/TPAMI.2005.109","volume":"27","author":"G Fumera","year":"2005","unstructured":"Fumera, G., Roli, F.: A theoretical and experimental analysis of linear combiners for multiple classifier systems. IEEE Trans. PAMI 27(6), 942\u2013956 (2005)","journal-title":"IEEE Trans. PAMI"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Bonab, H., Can, F.: Less is more: A comprehensive framework for the number of components of ensemble classifiers. IEEE Trans. on Neural Networks and Learning Systems 30(9) (2019) 2735\u20132745","DOI":"10.1109\/TNNLS.2018.2886341"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"van Erp, M., Vuurpijl, L., Schomaker, L.: An overview and comparison of voting methods for pattern recognition. In: Proceedings of Eighth International Workshop on Frontiers in Handwriting Recognition, pp. 195\u2013200 (2002)","DOI":"10.1109\/IWFHR.2002.1030908"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Liang, B., Zheng, L.: A survey on human action recognition using depth sensors. In: Int. Conf. on Digital Image Comp.: Techn. and Appl., pp. 1\u20138(2015)","DOI":"10.1109\/DICTA.2015.7371223"},{"key":"36_CR9","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/TIP.2019.2925285","volume":"29","author":"L Wang","year":"2020","unstructured":"Wang, L., Huynh, D.Q., Koniusz, P.: A comparative review of recent Kinect-based action recognition algorithms. IEEE Trans. Image Process. 29, 15\u201328 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"7824","key":"36_CR10","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1038\/s41586-020-2669-y","volume":"585","author":"A Haque","year":"2020","unstructured":"Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193\u2013202 (2020)","journal-title":"Nature"},{"key":"36_CR11","unstructured":"Ren, B., Liu, M., Ding, R., Liu, H.: A survey on 3D skeleton-based action recognition using learning method. arXiv, 2002.05907 (2020)"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhang, C., Tian, Y.L.: Recognizing actions using depth motion maps-based histograms of oriented gradients. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 1057\u20131060. ACM (2012)","DOI":"10.1145\/2393347.2396382"},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Xia, L., Aggarwal, J.: Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In: CVPR, pp. 2834\u20132841(2013)","DOI":"10.1109\/CVPR.2013.365"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Li, C., Huang, Q., Li, X., Wu, Q.: A multi-scale human action recognition method based on Laplacian pyramid depth motion images. In: Proceedings the 2nd ACM International Conference on Multimedia in Asia. ACM (2021)","DOI":"10.1145\/3444685.3446284"},{"issue":"3","key":"36_CR15","doi-asserted-by":"publisher","first-page":"2454","DOI":"10.1109\/JSEN.2020.3022326","volume":"21","author":"S Majumder","year":"2021","unstructured":"Majumder, S., Kehtarnavaz, N.: Vision and inertial sensing fusion for human action recognition: a review. IEEE Sensors J. 21(3), 2454\u20132467 (2021)","journal-title":"IEEE Sensors J."},{"key":"36_CR16","doi-asserted-by":"publisher","unstructured":"Trelinski, J., Kwolek, B.: Deep embedding features for action recognition on raw depth maps. In: Paszynski, M., Kranzlm\u00fcller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds.) ICCS 2021. LNCS, vol. 12744, pp. 95\u2013108. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-77967-2_9","DOI":"10.1007\/978-3-030-77967-2_9"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Hassan, S., Hemeida, A.M., Alkhalaf, S., Mohamed, A.A., Senjyu, T.: Multi-variant differential evolution algorithm for feature selection. Scientific Reports 10(1), October 2020","DOI":"10.1038\/s41598-020-74228-0"},{"issue":"4","key":"36_CR18","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1016\/j.jcss.2014.11.002","volume":"81","author":"G Erd\u00e9lyi","year":"2015","unstructured":"Erd\u00e9lyi, G., Fellows, M.R., Rothe, J., Schend, L.: Control complexity in Bucklin and fallback voting: a theoretical analysis. J. Comput. Syst. Sci. 81(4), 632\u2013660 (2015)","journal-title":"J. Comput. Syst. Sci."},{"key":"36_CR19","unstructured":"Pacuit, E.: Voting methods. Stanford Encyclopedia of Philosophy (Fall 2017 Ed.), Edward N. Zalta (ed.) (2017)"},{"issue":"4","key":"36_CR20","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/0165-1684(82)90009-3","volume":"4","author":"K Paliwal","year":"1982","unstructured":"Paliwal, K., Agarwal, A., Sinha, S.: A modification over Sakoe and Chiba\u2019s dynamic time warping algorithm for isolated word recognition. Signal Proc. 4(4), 329\u2013333 (1982)","journal-title":"Signal Proc."},{"key":"36_CR21","unstructured":"Meert, W., Hendrickx, K., Craenendonck, T.V.: DTAIdistance, ver. 2.0 (2021). https:\/\/zenodo.org\/record\/3981067"},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Hu, J., Zheng, W., Lai, J., Zhang, J.: Jointly learning heterogeneous features for RGB-D activity recognition. In: CVPR, pp. 5344\u20135352 (2015)","DOI":"10.1109\/CVPR.2015.7299172"},{"issue":"11","key":"36_CR23","doi-asserted-by":"publisher","first-page":"2568","DOI":"10.1109\/TPAMI.2018.2863279","volume":"41","author":"J Hu","year":"2019","unstructured":"Hu, J., Zheng, W., Ma, L., Wang, G., Lai, J., Zhang, J.: Early action prediction by soft regression. IEEE Trans. PAMI 41(11), 2568\u20132583 (2019)","journal-title":"IEEE Trans. PAMI"},{"key":"36_CR24","doi-asserted-by":"crossref","unstructured":"Wang, X., Hu, J.F., Lai, J.H., Zhang, J., Zheng, W.S.: Progressive teacher-student learning for early action prediction. In: CVPR, pp. 3551\u20133560 (2019)","DOI":"10.1109\/CVPR.2019.00367"},{"key":"36_CR25","unstructured":"Zhang, P., Lan, C., Zeng, W., Xing, J., Xue, J., Zheng, N.: Semantics-guided neural networks for efficient skeleton-based human action recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1109\u20131118. IEEE"},{"key":"36_CR26","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1109\/TIP.2019.2937757","volume":"29","author":"Q Ke","year":"2020","unstructured":"Ke, Q., Bennamoun, M., Rahmani, H., An, S., Sohel, F., Boussaid, F.: Learning latent global network for skeleton-based action prediction. IEEE Trans. Img. Proc. 29, 959\u2013970 (2020)","journal-title":"IEEE Trans. Img. Proc."},{"key":"36_CR27","doi-asserted-by":"crossref","unstructured":"Hu, J.F., Zheng, W.S., Ma, L., Wang, G., Lai, J.: Real-time RGB-D activityprediction by soft regression. In: European Conf. on Comp. Vision, pp. 280\u2013296. Springer (2016)","DOI":"10.1007\/978-3-319-46448-0_17"},{"issue":"11","key":"36_CR28","doi-asserted-by":"publisher","first-page":"16185","DOI":"10.1007\/s11042-019-08576-z","volume":"80","author":"Z Ren","year":"2020","unstructured":"Ren, Z., Zhang, Q., Gao, X., Hao, P., Cheng, J.: Multi-modality learning for human action recognition. Multimed. Tools Appl. 80(11), 16185\u201316203 (2020). https:\/\/doi.org\/10.1007\/s11042-019-08576-z","journal-title":"Multimed. Tools Appl."}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88081-1_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:27:31Z","timestamp":1725841651000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88081-1_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030880804","9783030880811"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88081-1_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rhodos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"231","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}