{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:15:44Z","timestamp":1753884944575,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":16,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811623790"},{"type":"electronic","value":"9789811623806"}],"license":[{"start":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T00:00:00Z","timestamp":1631836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T00:00:00Z","timestamp":1631836800000},"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":[[2022]]},"DOI":"10.1007\/978-981-16-2380-6_32","type":"book-chapter","created":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T09:02:47Z","timestamp":1631782967000},"page":"367-375","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Effective Feature Selection Using Ensemble Techniques and Genetic Algorithm"],"prefix":"10.1007","author":[{"given":"Jayshree","family":"Ghorpade-Aher","sequence":"first","affiliation":[]},{"given":"Balwant","family":"Sonkamble","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,17]]},"reference":[{"issue":"2","key":"32_CR1","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/TCYB.2018.2859342","volume":"50","author":"W Ding","year":"2020","unstructured":"Ding W, Lin C, Pedrycz W (2020) Multiple relevant feature ensemble selection based on multilayer co-evolutionary consensus mapreduce. IEEE Trans Cybern 50(2):425\u2013439","journal-title":"IEEE Trans Cybern"},{"key":"32_CR2","doi-asserted-by":"crossref","unstructured":"Zhao Y, Duangsoithong R (2020) Empirical analysis using feature selection and bootstrap data for small sample size problems. In: IEEE 16th international conference on electrical engineering\/electronics, computer, telecommunications and information technology (ECTI-CON), Pattaya, Chonburi, Thailand, pp 814\u2013817. (January 2020)","DOI":"10.1109\/ECTI-CON47248.2019.8955366"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Ghorpade J, Sonkamble B (2020) Predictive analysis of heterogeneous data\u2013techniques & tools. In:\u00a02020 5th international conference on computer and communication systems (ICCCS), Shanghai, China, pp 40\u201344. (May 2020)","DOI":"10.1109\/ICCCS49078.2020.9118578"},{"key":"32_CR4","unstructured":"Pes B (2019) Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains. In: Neural computing and applications. Springer pp 1\u201312. (February 2019)"},{"issue":"2","key":"32_CR5","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1080\/21642583.2019.1620658","volume":"7","author":"J Wang","year":"2019","unstructured":"Wang J, Xu J, Zhao C, Peng Y, Wang H (2019) An ensemble feature selection method for high-dimensional data based on sort aggregation. Syst Sci Control Eng IEEE Access 7(2):32\u201339","journal-title":"Syst Sci Control Eng IEEE Access"},{"key":"32_CR6","unstructured":"Woodward A (2020) What to know about the coronavirus outbreak. World Economic Forum. (March 2020)"},{"key":"32_CR7","unstructured":"Yamada Y, Lindenbaum O, Negahban S, Kluger Y (2020) Feature selection using stochastic gates. In: 37th international conference on machine learning, Vienna, Austria, PMLR 119, pp 1\u201312. (July 2020)"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Khair U, Lestari YD, Perdana A, Hidayat D, Budiman A (2019) Genetic algorithm modification analysis of mutation operators in max one problem. In: IEEE, third international conference on informatics and computing (ICIC), Palembang, Indonesia, pp 1\u20136 (August 2019)","DOI":"10.1109\/IAC.2018.8780463"},{"issue":"2","key":"32_CR9","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1109\/TCYB.2017.2761908","volume":"49","author":"Z Yu","year":"2019","unstructured":"Yu Z et al (2019) Adaptive semi-supervised classifier ensemble for high dimensional data classification. IEEE Trans Cybern 49(2):366\u2013379","journal-title":"IEEE Trans Cybern"},{"issue":"3","key":"32_CR10","first-page":"454","volume":"24","author":"K Nag","year":"2020","unstructured":"Nag K, Pal NR (2020) Feature extraction and selection for parsimonious classifiers with multiobjective genetic programming. IEEE Trans Evol Comput 24(3):454\u2013466","journal-title":"IEEE Trans Evol Comput"},{"issue":"5","key":"32_CR11","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/TLA.2018.8408446","volume":"16","author":"P Palhares","year":"2018","unstructured":"Palhares P, Brito L (2018) Constrained mixed integer programming solver based on the compact genetic algorithm. IEEE Latin Am Trans 16(5):1493\u20131498","journal-title":"IEEE Latin Am Trans"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Aruna Kumari GL, Padmaja P, Jaya Suma G (2020) ENN-ensemble based neural network method for diabetes classification. Int J Eng Adv Technol (IJEAT) 9(3). ISSN: 2249\u20138958","DOI":"10.35940\/ijeat.C4819.029320"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H (2018) Feature selection: a data perspective. ACM Comput Surv (CSUR) 50(06), pp 1\u201345","DOI":"10.1145\/3136625"},{"issue":"9","key":"32_CR14","doi-asserted-by":"publisher","first-page":"4504","DOI":"10.1109\/TNNLS.2017.2746107","volume":"29","author":"G Ditzler","year":"2018","unstructured":"Ditzler G, LaBarck J, Ritchie J, Rosen G, Polikar R (2018) Extensions to online feature selection using bagging and boosting. IEEE Trans Neural Netw Learn Syst 29(9):4504\u20134509","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Thomas J, Sael L (2015) Overview of integrative analysis methods for heterogeneous data. In: IEEE international conference on big data and smart computing, Jeju, pp 266\u2013270","DOI":"10.1109\/35021BIGCOMP.2015.7072811"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Seijo-PardoI B, Porto-D\u00edaz I, Bol\u00f3n-Canedo V, Alonso-Betanzos A (2017) Ensemble feature selection: homogeneous and heterogeneous approaches. In: Knowledge-based systems, vol 118. Elsevier, pp 124\u2013139 (February 2017)","DOI":"10.1016\/j.knosys.2016.11.017"}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of Sixth International Congress on Information and Communication Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-2380-6_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T09:24:25Z","timestamp":1631784265000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-2380-6_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,17]]},"ISBN":["9789811623790","9789811623806"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-2380-6_32","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2021,9,17]]},"assertion":[{"value":"17 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}