{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:02:47Z","timestamp":1743116567312,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811534249"},{"type":"electronic","value":"9789811534256"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-3425-6_48","type":"book-chapter","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T19:02:58Z","timestamp":1585767778000},"page":"611-621","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-objective Feature Selection Based on Artificial Bee Colony for Hyperspectral Images"],"prefix":"10.1007","author":[{"given":"Chun-lin","family":"He","sequence":"first","affiliation":[]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Dun-wei","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,2]]},"reference":[{"issue":"1","key":"48_CR1","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","volume":"14","author":"G Hughes","year":"1968","unstructured":"Hughes, G.: On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 14(1), 55\u201363 (1968)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"6","key":"48_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2017","unstructured":"Li, J., et al.: Feature selection. ACM Comput. Surv. 50(6), 1\u201345 (2017)","journal-title":"ACM Comput. Surv."},{"issue":"12","key":"48_CR3","doi-asserted-by":"publisher","first-page":"4158","DOI":"10.1109\/TGRS.2007.904951","volume":"45","author":"A Martinez-Uso","year":"2008","unstructured":"Martinez-Uso, A., Pla, F., Sotoca, J.M.: Clustering-based hyperspectral band selection using information measures. IEEE Trans. Geosci. Remote Sens. 45(12), 4158\u20134171 (2008)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"48_CR4","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1109\/LGRS.2010.2091673","volume":"8","author":"C Cariou","year":"2011","unstructured":"Cariou, C., Chehdi, K., Moan, S.: BandClust: an unsupervised band reduction method for hyperspectral remote sensing. IEEE Geosci. Remote Sens. Lett. 8(3), 565\u2013569 (2011)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"6","key":"48_CR5","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1109\/LGRS.2011.2158185","volume":"8","author":"H Su","year":"2011","unstructured":"Su, H., Yang, H., Du, Q.: Semi-supervised band clustering for dimensionality reduction of hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 8(6), 1135\u20131139 (2011)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"2","key":"48_CR6","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1109\/JSTARS.2012.2187434","volume":"5","author":"S Jia","year":"2012","unstructured":"Jia, S., Zhen, J., Qian, Y.: Unsupervised band selection for hyperspectral imagery classification without manual band removal. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(2), 531\u2013543 (2012)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"issue":"4","key":"48_CR7","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/TEVC.2017.2744674","volume":"22","author":"X Cai","year":"2018","unstructured":"Cai, X., Mei, Z., Fan, Z., Zhang, Q.: A constrained decomposition approach with grids for evolutionary multi-objective optimization. IEEE Trans. Evol. Comput. 22(4), 99\u2013104 (2018)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"48_CR8","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/TEVC.2018.2802784","volume":"23","author":"L Pan","year":"2018","unstructured":"Pan, L., He, C., Tian, Y., Wang, H., Zhang, X., Jin, Y.: A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Trans. Evol. Comput. 23(1), 74\u201388 (2018)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"48_CR9","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.asoc.2017.08.024","volume":"61","author":"C He","year":"2017","unstructured":"He, C., Tian, Y., Jin, Y., Zhang, X., Pan, L.: A radial space division based evolutionary algorithm for many-objective optimization. Appl. Soft Comput. 61, 603\u2013621 (2017)","journal-title":"Appl. Soft Comput."},{"key":"48_CR10","unstructured":"Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Erciyes University, Kayseri (2005)"},{"issue":"12","key":"48_CR11","doi-asserted-by":"publisher","first-page":"4158","DOI":"10.1109\/TGRS.2007.904951","volume":"45","author":"C Chang","year":"2007","unstructured":"Chang, C., Wang, S.: Constrained band selection for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 45(12), 4158\u20134171 (2007)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"12","key":"48_CR12","doi-asserted-by":"publisher","first-page":"4158","DOI":"10.1109\/TGRS.2007.904951","volume":"45","author":"A Mart\u00ednez-Uso","year":"2007","unstructured":"Mart\u00ednez-Uso, A., Pla, F., Sotoca, J.M.: Clustering-based hyperspectral band selection using information measures. IEEE Trans. Geosci. Remote Sens. 45(12), 4158\u20134171 (2007)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"48_CR13","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","volume":"20","author":"B Xue","year":"2016","unstructured":"Xue, B., Zhang, M., Browne, W., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606\u2013626 (2016)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"419","key":"48_CR14","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.ins.2017.08.047","volume":"418","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Song, X., Gong, D.: A return-cost-based binary firefly algorithm for feature selection. Inf. Sci. 418(419), 561\u2013574 (2017)","journal-title":"Inf. Sci."},{"issue":"1","key":"48_CR15","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1109\/TCBB.2015.2476796","volume":"14","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Gong, D., Cheng, J.: Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE\/ACM Trans. Comput. Biol. Bioinf. 14(1), 64\u201375 (2017)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"48_CR16","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.eswa.2019.06.044","volume":"137","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Cheng, S., Gong, D., Shi, Y., Zhao, X.: Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm. Expert Syst. Appl. 137, 46\u201358 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"8","key":"48_CR17","doi-asserted-by":"publisher","first-page":"2889","DOI":"10.1007\/s10489-019-01420-9","volume":"49","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Li, H., Wang, Q.: A filter-based bare-bone particle swarm optimization algorithm for unsupervised feature selection. Appl. Intell. 49(8), 2889\u20132898 (2019)","journal-title":"Appl. Intell."},{"issue":"6","key":"48_CR18","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1109\/TSMCB.2012.2227469","volume":"43","author":"B Xue","year":"2013","unstructured":"Xue, B., Zhang, M., Browne, W.: Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. Cybern. 43(6), 1656\u20131671 (2013)","journal-title":"IEEE Trans. Cybern."},{"issue":"5","key":"48_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3340848","volume":"13","author":"Y Xue","year":"2019","unstructured":"Xue, Y., Xue, B., Zhang, M.: Self-adaptive particle swarm optimization for large-scale feature selection in classification. ACM Trans. Knowl. Discov. Data 13(5), 1\u201328 (2019)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"issue":"12","key":"48_CR20","doi-asserted-by":"publisher","first-page":"4175","DOI":"10.1109\/TGRS.2009.2023666","volume":"47","author":"A Paoli","year":"2009","unstructured":"Paoli, A., Melgani, F., Pasolli, E.: Clustering of hyperspectral images based on multi-objective particle swarm optimization. IEEE Trans. Geosci. Remote Sens. 47(12), 4175\u20134188 (2009)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"53","key":"48_CR21","first-page":"631","volume":"2","author":"Y Zhu","year":"2015","unstructured":"Zhu, Y.: Hyperspectral band selection by multitask sparsity pursuit. IEEE Trans. Geosci. Remote Sens. 2(53), 631\u2013644 (2015)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"5","key":"48_CR22","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1109\/LGRS.2017.2681118","volume":"14","author":"M Zhang","year":"2017","unstructured":"Zhang, M., Ma, J., Gong, M.: Unsupervised hyperspectral band selection by fuzzy clustering with particle swarm optimization. IEEE Geosci. Remote Sens. Lett. 14(5), 773\u2013777 (2017)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"C","key":"48_CR23","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.patcog.2015.08.018","volume":"51","author":"J Feng","year":"2016","unstructured":"Feng, J., Jiao, L., Liu, F.: Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images. Pattern Recogn. 51(C), 295\u2013309 (2016)","journal-title":"Pattern Recogn."},{"key":"48_CR24","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1016\/j.asoc.2018.06.009","volume":"70","author":"M Zhang","year":"2018","unstructured":"Zhang, M., Gong, M., Chan, Y.: Hyperspectral band selection based on multi-objective optimization with high information and low redundancy. Appl. Soft Comput. 70, 604\u2013621 (2018)","journal-title":"Appl. Soft Comput."},{"key":"48_CR25","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.ins.2014.12.043","volume":"300","author":"M Kiran","year":"2015","unstructured":"Kiran, M., Hakli, H., Gunduz, M.: Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf. Sci. 300, 140\u2013157 (2015)","journal-title":"Inf. Sci."},{"issue":"2","key":"48_CR26","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1109\/TEVC.2014.2308305","volume":"19","author":"X Zhang","year":"2015","unstructured":"Zhang, X., Tian, Y., Cheng, R.: An efficient approach to nondominated sorting for evolutionary multi-objective optimization. IEEE Trans. Evol. Comput. 19(2), 201\u2013213 (2015)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"2","key":"48_CR27","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S.: A fast and elitist multi-objective genetic algorithm NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"9","key":"48_CR28","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1007\/s00500-017-2547-1","volume":"22","author":"Y Xue","year":"2018","unstructured":"Xue, Y., Jiang, J., Zhao, B., Ma, T.: A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft. Comput. 22(9), 2935\u20132952 (2018)","journal-title":"Soft. Comput."}],"container-title":["Communications in Computer and Information Science","Bio-inspired Computing: Theories and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-3425-6_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T01:34:15Z","timestamp":1585791255000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-3425-6_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811534249","9789811534256"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-3425-6_48","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"2 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIC-TA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bio-Inspired Computing: Theories and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bicta2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2019.bicta.org","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":"197","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":"121","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":"61% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}