{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T07:26:55Z","timestamp":1745825215563,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030557881"},{"type":"electronic","value":"9783030557898"}],"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"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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\/978-3-030-55789-8_40","type":"book-chapter","created":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T23:07:57Z","timestamp":1599174477000},"page":"457-469","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The Differential Feature Detection and the Clustering Analysis to Breast Cancers"],"prefix":"10.1007","author":[{"given":"Juanying","family":"Xie","sequence":"first","affiliation":[]},{"given":"Zhaozhong","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Qin","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Lijuan","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Hamido","family":"Fujita","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,4]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Cai, D., Zhang, C., He, X.: Unsupervised feature selection for multi-cluster data. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 333\u2013342. ACM (2010)","DOI":"10.1145\/1835804.1835848"},{"issue":"7","key":"40_CR2","first-page":"14","volume":"35","author":"X Cai","year":"2008","unstructured":"Cai, X., Dai, G., Yang, L.: Survey on spectral clustering algorithms. Comput. Sci. 35(7), 14\u201318 (2008)","journal-title":"Comput. Sci."},{"issue":"3","key":"40_CR3","first-page":"27","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"issue":"4","key":"40_CR4","first-page":"1485","volume":"41","author":"Z Deng","year":"2010","unstructured":"Deng, Z., Tan, G., Ye, J., Fan, B.: An immune classification algorithm for breast cancer diagnosis. J. Central South Univ. (Sci. Technol.) 41(4), 1485\u20131490 (2010)","journal-title":"J. Central South Univ. (Sci. Technol.)"},{"key":"40_CR5","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017). http:\/\/archive.ics.uci.edu\/ml"},{"key":"40_CR6","unstructured":"He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: Advances in Neural Information Processing Systems, pp. 507\u2013514 (2006)"},{"issue":"4","key":"40_CR7","first-page":"519","volume":"12","author":"M Hu","year":"2017","unstructured":"Hu, M., Lin, Y., Yang, H., Zheng, L., Fu, W.: Spectral feature selection based on feature correlation. CAAI Trans. Intell. Syst. 12(4), 519\u2013525 (2017)","journal-title":"CAAI Trans. Intell. Syst."},{"issue":"1","key":"40_CR8","doi-asserted-by":"publisher","first-page":"75","DOI":"10.2478\/v10006-008-0007-x","volume":"18","author":"L Jele\u0144","year":"2008","unstructured":"Jele\u0144, L., Fevens, T., Krzy\u017cak, A.: Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies. Int. J. Appl. Math. Comput. Sci. 18(1), 75\u201383 (2008)","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"issue":"11","key":"40_CR9","first-page":"255","volume":"40","author":"H Jiang","year":"2013","unstructured":"Jiang, H., Yu, X.: Ga-based subspace classification algorithm for support vector machines. Comput. Sci. 40(11), 255\u2013260 (2013)","journal-title":"Comput. Sci."},{"issue":"5","key":"40_CR10","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.1109\/TSMCB.2007.903194","volume":"37","author":"K Leung","year":"2007","unstructured":"Leung, K., Cheong, F., Cheong, C.: Generating compact classifier systems using a simple artificial immune system. IEEE Trans. Syst. Man Cybern. Part B Cybern. 37(5), 1344\u20131356 (2007)","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"40_CR11","unstructured":"MacQueen, J.B.: Some methods for the classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1:Statistics, pp. 281\u2013297. University of California Press, Berkeley (1967)"},{"key":"40_CR12","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.imu.2017.12.008","volume":"10","author":"ON Oyelade","year":"2018","unstructured":"Oyelade, O.N., Obiniyi, A.A., Junaidu, S.B., Adewuyi, A.S.: ST-ONCODIAG: a semantic rule-base approach to diagnosing breast cancer base on wisconsin datasets. Inform. Med. Unlocked 10, 117\u2013125 (2018)","journal-title":"Inform. Med. Unlocked"},{"issue":"6191","key":"40_CR13","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodr\u00edguez","year":"2014","unstructured":"Rodr\u00edguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492\u20131496 (2014)","journal-title":"Science"},{"key":"40_CR14","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1038\/s41598-018-37044-1","volume":"9","author":"L Shen","year":"2019","unstructured":"Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R.B., Sieh, W.: Deep learning to improve breast cancer early detection on screening mammography. Sci. rep. 9, 12 (2019)","journal-title":"Sci. rep."},{"issue":"10","key":"40_CR15","doi-asserted-by":"publisher","first-page":"2915","DOI":"10.1007\/s00521-017-2959-y","volume":"28","author":"S Tiwari","year":"2017","unstructured":"Tiwari, S., Singh, B., Kaur, M.: An approach for feature selection using local searching and global optimization techniques. Neural Comput. Appl. 28(10), 2915\u20132930 (2017). https:\/\/doi.org\/10.1007\/s00521-017-2959-y","journal-title":"Neural Comput. Appl."},{"issue":"4","key":"40_CR16","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"VL Ulrike","year":"2007","unstructured":"Ulrike, V.L.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395\u2013416 (2007)","journal-title":"Stat. Comput."},{"issue":"3","key":"40_CR17","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1023\/B:GENP.0000030197.83685.94","volume":"5","author":"A Watkins","year":"2004","unstructured":"Watkins, A., Timmis, J., Boggess, L.: Artificial immune recognition system (AIRS): an immune-inspired supervised learning algorithm. Genet. Program Evolvable Mach. 5(3), 291\u2013317 (2004)","journal-title":"Genet. Program Evolvable Mach."},{"issue":"05","key":"40_CR18","first-page":"1000","volume":"47","author":"J Xie","year":"2019","unstructured":"Xie, J., Ding, L.: The true self-adaptive spectral clustering algorithms. Acta Electronic Sinica 47(05), 1000\u20131008 (2019)","journal-title":"Acta Electronic Sinica"},{"issue":"8","key":"40_CR19","first-page":"973","volume":"9","author":"J Xie","year":"2015","unstructured":"Xie, J., Gao, R.: K-medoids clustering algorithms with optimized initial seeds by variance. J. Front. Comput. Sci. Technol. 9(8), 973\u2013984 (2015)","journal-title":"J. Front. Comput. Sci. Technol."},{"key":"40_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/978-3-319-68935-7_33","volume-title":"Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2017","author":"J Xie","year":"2017","unstructured":"Xie, J., Jiang, W., Ding, L.: Clustering by searching density peaks via local standard deviation. In: Yin, H., et al. (eds.) IDEAL 2017. LNCS, vol. 10585, pp. 295\u2013305. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68935-7_33"},{"key":"40_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/978-3-319-48335-1_21","volume-title":"Health Information Science","author":"J Xie","year":"2016","unstructured":"Xie, J., Li, Y., Zhou, Y., Wang, M.: Differential feature recognition of breast cancer patients based on minimum spanning tree clustering and f-statistics. In: Yin, X., Geller, J., Li, Y., Zhou, R., Wang, H., Zhang, Y. (eds.) HIS 2016. LNCS, vol. 10038, pp. 194\u2013204. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48335-1_21"},{"key":"40_CR22","doi-asserted-by":"publisher","first-page":"80","DOI":"10.3389\/fgene.2019.00080","volume":"10","author":"J Xie","year":"2019","unstructured":"Xie, J., Liu, R., Luttrell, J., Zhang, C.: Deep learning based analysis of histopathological images of breast cancer. Front. Genetics 10, 80 (2019)","journal-title":"Front. Genetics"},{"issue":"4","key":"40_CR23","first-page":"735","volume":"52","author":"J Xie","year":"2016","unstructured":"Xie, J., Qu, Y., Wang, M.: Unsupervised feature selection algorithms based on density peaks. J. Nanjing Univ. (Nat. Sci.) 52(4), 735\u2013745 (2016)","journal-title":"J. Nanjing Univ. (Nat. Sci.)"},{"issue":"6","key":"40_CR24","first-page":"1232","volume":"42","author":"J Xie","year":"2019","unstructured":"Xie, J., Wang, M., Zhou, Y., Gao, H., Xu, S.: Differentially expressed gene selection algorithms for unbalanced gene datasets. Chin. J. Comput. 42(6), 1232\u20131251 (2019)","journal-title":"Chin. J. Comput."},{"issue":"4","key":"40_CR25","first-page":"14","volume":"21","author":"X Ye","year":"2013","unstructured":"Ye, X., Wang, S.: Comparative study on the performances of Bayesian classification and LVQ neural network. Comput. Inform. Technol. 21(4), 14\u201317 (2013)","journal-title":"Comput. Inform. Technol."},{"issue":"5","key":"40_CR26","first-page":"95","volume":"30","author":"C Zhang","year":"2013","unstructured":"Zhang, C., Wei, S., Hu, X., et al.: Research and application of lvq neural network based on particle swarm optimization algorithm. J. Guizhou Univ. (Nat. Sci.) 30(5), 95\u201399 (2013)","journal-title":"J. Guizhou Univ. (Nat. Sci.)"},{"issue":"8","key":"40_CR27","first-page":"561","volume":"23","author":"Y Zhang","year":"2013","unstructured":"Zhang, Y., Wu, C., Zhang, M.: The epidemic and characteristics of female breast cancer in china. China Oncol. 23(8), 561\u2013569 (2013)","journal-title":"China Oncol."},{"issue":"8","key":"40_CR28","first-page":"2373","volume":"30","author":"Y Zhang","year":"2013","unstructured":"Zhang, Y., Shi, H., Shang, W., Xiaofeng, J.X.Z.: Improved method for computer-aided diagnosis of breast cancer based on support vector machines. Appl. Res. Comput. 30(8), 2373\u20132376 (2013)","journal-title":"Appl. Res. Comput."},{"issue":"6","key":"40_CR29","first-page":"1599","volume":"37","author":"C Zheng","year":"2016","unstructured":"Zheng, C., Hong, W., Wang, J.: Rule extraction method of breast cancer diagnosis based on partial orderd structure diagram. Comput. Eng. Des. 37(6), 1599\u20131603 (2016)","journal-title":"Comput. Eng. Des."}],"container-title":["Lecture Notes in Computer Science","Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-55789-8_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T10:25:07Z","timestamp":1608027907000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-55789-8_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030557881","9783030557898"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-55789-8_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"4 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IEA\/AIE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kitakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"33","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ieaaie2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/jsasaki3.wixsite.com\/ieaaie2020\/organizations","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"119","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":"62","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":"17","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":"52% - 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":"4,35","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)"}}]}}