{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T23:01:46Z","timestamp":1779922906422,"version":"3.53.1"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032214799","type":"print"},{"value":"9783032214805","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-21480-5_5","type":"book-chapter","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T22:02:00Z","timestamp":1779919320000},"page":"63-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimizing Permutation-Invariant Criterion with\u00a0Law-Smooth Cross-Entropy Method: Application to\u00a0Spectral Band Selection for\u00a0Anomaly Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1460-4126","authenticated-orcid":false,"given":"Fr\u00e9d\u00e9ric","family":"Dambreville","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8332-972X","authenticated-orcid":false,"given":"Sidonie","family":"Lefebvre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"5_CR1","unstructured":"Dambreville, F.: Cross-entropy method: convergence issues for extended implementation (2006). https:\/\/arxiv.org\/abs\/math\/0609461"},{"key":"5_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.107991","volume":"216","author":"M El Masri","year":"2021","unstructured":"El Masri, M., Morio, J., Simatos, F.: Improvement of the cross-entropy method in high dimension for failure probability estimation through a one-dimensional projection without gradient estimation. Reliab. Eng. Syst. Saf. 216, 107991 (2021). https:\/\/doi.org\/10.1016\/j.ress.2021.107991","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"102","key":"5_CR3","first-page":"36","volume":"1989","author":"DE Golberg","year":"1989","unstructured":"Golberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison Wesley 1989(102), 36 (1989)","journal-title":"Addison Wesley"},{"key":"5_CR4","doi-asserted-by":"publisher","unstructured":"Hansen, N., Arnold, D.V., Auger, A.: Evolution strategies, pp. 871\u2013898. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-662-43505-2_44","DOI":"10.1007\/978-3-662-43505-2_44"},{"issue":"1","key":"5_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/106365603321828970","volume":"11","author":"N Hansen","year":"2003","unstructured":"Hansen, N., M\u00fcller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 11(1), 1\u201318 (2003). https:\/\/doi.org\/10.1162\/106365603321828970","journal-title":"Evol. Comput."},{"key":"5_CR6","doi-asserted-by":"publisher","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN\u201995 - International Conference on Neural Networks, vol.\u00a04, pp. 1942\u20131948 (1995). https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"3","key":"5_CR7","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s11009-006-9753-0","volume":"8","author":"DP Kroese","year":"2006","unstructured":"Kroese, D.P., Porotsky, S., Rubinstein, R.Y.: The cross-entropy method for continuous multi-extremal optimization. Method. Comput. Appl. Prob. 8(3), 383\u2013407 (2006)","journal-title":"Method. Comput. Appl. Prob."},{"key":"5_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3258067","volume":"61","author":"Z Li","year":"2023","unstructured":"Li, Z., Wang, Y., Xiao, C., Ling, Q., Lin, Z., An, W.: You only train once: learning a general anomaly enhancement network with random masks for hyperspectral anomaly detection. IEEE Trans. Geosci. Remote Sens. 61, 1\u201318 (2023). https:\/\/doi.org\/10.1109\/TGRS.2023.3258067","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"5_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tgrs.2023.3258067","volume":"61","author":"Z Li","year":"2023","unstructured":"Li, Z., Wang, Y., Xiao, C., Ling, Q., Lin, Z., An, W.: You only train once: learning a general anomaly enhancement network with random masks for hyperspectral anomaly detection. IEEE Trans. Geosci. Remote Sens. 61, 1\u201318 (2023). https:\/\/doi.org\/10.1109\/tgrs.2023.3258067","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"10","key":"5_CR10","doi-asserted-by":"publisher","first-page":"1760","DOI":"10.1109\/29.60107","volume":"38","author":"I Reed","year":"1990","unstructured":"Reed, I., Yu, X.: Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Trans. Acoust. Speech Signal Process. 38(10), 1760\u20131770 (1990). https:\/\/doi.org\/10.1109\/29.60107","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Rubinstein, R.Y., Kroese, D.P.: The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-Carlo Simulation (Information Science and Statistics). Springer (2004)","DOI":"10.1007\/978-1-4757-4321-0_4"},{"key":"5_CR12","doi-asserted-by":"publisher","unstructured":"Vijayashekhar, S.S., Bhatt, J.S., Chattopadhyay, B.: Virtual dimensionality of hyperspectral data: use of multiple hypothesis testing for controlling type-i error. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 2974\u20132985 (2020). https:\/\/doi.org\/10.1109\/JSTARS.2020.2991170","DOI":"10.1109\/JSTARS.2020.2991170"},{"issue":"1","key":"5_CR13","doi-asserted-by":"publisher","first-page":"1968","DOI":"10.1038\/s41598-024-84934-8","volume":"15","author":"Y Shang","year":"2025","unstructured":"Shang, Y., Zheng, M., Li, J., Zheng, X.: An effective feature selection approach based on hybrid grey wolf optimizer and genetic algorithm for hyperspectral image. Sci. Rep. 15(1), 1968 (2025). https:\/\/doi.org\/10.1038\/s41598-024-84934-8","journal-title":"Sci. Rep."},{"key":"5_CR14","doi-asserted-by":"publisher","unstructured":"Shang, Y., Zheng, X., Li, J., Liu, D., Wang, P.: A comparative analysis of swarm intelligence and evolutionary algorithms for feature selection in SVM-based hyperspectral image classification. Remote Sens. 14(13) (2022). https:\/\/doi.org\/10.3390\/rs14133019. https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3019","DOI":"10.3390\/rs14133019"},{"key":"5_CR15","doi-asserted-by":"publisher","unstructured":"Storn, R., Price, K.: Differential evolution \u2013 a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341\u2013359 (1997). https:\/\/doi.org\/10.1023\/A:1008202821328","DOI":"10.1023\/A:1008202821328"},{"issue":"6","key":"5_CR16","doi-asserted-by":"publisher","first-page":"2659","DOI":"10.1109\/JSTARS.2014.2312539","volume":"7","author":"H Su","year":"2014","unstructured":"Su, H., Du, Q., Chen, G., Du, P.: Optimized hyperspectral band selection using particle swarm optimization. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2659\u20132670 (2014). https:\/\/doi.org\/10.1109\/JSTARS.2014.2312539","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"5_CR17","doi-asserted-by":"publisher","unstructured":"Xiaohui, D., Huapeng, L., Yong, L., Ji, Y., Shuqing, Z.: Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image. Open Geosci. 12(1), 425\u2013442 (2020). https:\/\/doi.org\/10.1515\/geo-2020-0155","DOI":"10.1515\/geo-2020-0155"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-21480-5_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T22:02:07Z","timestamp":1779919327000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-21480-5_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032214799","9783032214805"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-21480-5_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Artificial Intelligence Symposium","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Castiglione della Pescaia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2025.icas.events","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}