{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T10:45:37Z","timestamp":1760784337698,"version":"3.37.3"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2017,8,22]],"date-time":"2017-08-22T00:00:00Z","timestamp":1503360000000},"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":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2019,2]]},"DOI":"10.1007\/s13042-017-0718-0","type":"journal-article","created":{"date-parts":[[2017,8,22]],"date-time":"2017-08-22T09:31:54Z","timestamp":1503394314000},"page":"337-345","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["N-semble: neural network based ensemble approach"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9861-8495","authenticated-orcid":false,"given":"Rishith","family":"Rayal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Divya","family":"Khanna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jasminder Kaur","family":"Sandhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nishtha","family":"Hooda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prashant Singh","family":"Rana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,8,22]]},"reference":[{"key":"718_CR1","unstructured":"Sourceforge. \n                    http:\/\/bit.ly\/RF-PCP-DataSets"},{"issue":"4","key":"718_CR2","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TNB.2016.2555951","volume":"15","author":"B Liu","year":"2016","unstructured":"Liu B, Wang S, Dong Q, Li S, Liu X (2016) Identification of DNA-binding proteins by combining auto-cross covariance transformation and ensemble learning. IEEE Trans Nano Biosci 15(4):328\u2013334","journal-title":"IEEE Trans Nano Biosci"},{"issue":"16","key":"718_CR3","doi-asserted-by":"publisher","first-page":"2411","DOI":"10.1093\/bioinformatics\/btw186","volume":"32","author":"B Liu","year":"2016","unstructured":"Liu B, Long R, Chou KC (2016) iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework. Bioinformatics 32(16):2411\u20132418","journal-title":"Bioinformatics"},{"issue":"1","key":"718_CR4","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1093\/bioinformatics\/btw539","volume":"33","author":"B Liu","year":"2017","unstructured":"Liu B, Long R, Chou KC (2017) iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics 33(1):35\u201341","journal-title":"Bioinformatics"},{"issue":"4","key":"718_CR5","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1093\/bioinformatics\/btt709","volume":"30","author":"B Liu","year":"2014","unstructured":"Liu B, Zhang D, Xu R, Xu J, Wang X, Chen Q, Dong Q, Chou KC (2014) Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics 30(4):472\u2013479","journal-title":"Bioinformatics"},{"key":"718_CR6","unstructured":"Cortes C, Mohri M, Rostamizadeh A (2016) Two-stage learning kernel algorithms. Proc 27th Int Conf Mach Learning (ICML-10) 62(3):1485\u20131500"},{"key":"718_CR7","doi-asserted-by":"crossref","unstructured":"Varma M, Bodla RB (2009) More generality in efficient multiple kernel learning. In: Proceedings of the 26th annual international conference on machine learning. ACM","DOI":"10.1145\/1553374.1553510"},{"issue":"6","key":"718_CR8","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/TKDE.2004.11","volume":"16","author":"Z-H Zhou","year":"2004","unstructured":"Zhou Z-H, Jiang Y (2004) NeC4.5: neural ensemble based C4.5. IEEE Trans Knowl Data Eng 16(6):770\u2013773","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"718_CR9","doi-asserted-by":"crossref","unstructured":"Pantola P, Bala A, Rana PS (2015) Consensus based ensemble model for spam detection. 2015 international conference on advances in computing, communications and informatics (ICACCI)","DOI":"10.1109\/ICACCI.2015.7275862"},{"issue":"2","key":"718_CR10","doi-asserted-by":"publisher","first-page":"1550005","DOI":"10.1142\/S0219720015500055","volume":"13","author":"PS Rana","year":"2015","unstructured":"Rana PS, Sharma H, Bhattacharya M, Shukla A (2015) Quality assessment of modelled protein structure using physicochemical properties. J Bioinf Comput Biol 13(2):1550005","journal-title":"J Bioinf Comput Biol"},{"issue":"3","key":"718_CR11","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1109\/TIT.2016.2514489","volume":"62","author":"E Scornet","year":"2016","unstructured":"Scornet E (2016) Random forests and kernel methods. IEEE Trans Inf Theory 62(3):1485\u20131500","journal-title":"IEEE Trans Inf Theory"},{"issue":"6","key":"718_CR12","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1109\/TCBB.2015.2418773","volume":"12","author":"X Ma","year":"2015","unstructured":"Ma X, Guo J, Xiao K, Sun X (2015) PRBP: prediction of RNA-binding proteins using a random forest algorithm combined with an RNA-binding residue predictor. IEEE\/ACM Trans Comput Biol Bioinf 12(6):1385\u20131393","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"6","key":"718_CR13","first-page":"649","volume":"14","author":"H Lin","year":"2015","unstructured":"Lin H et al (2015) Weighing fusion method for truck scales based on prior knowledge and neural network ensembles. IEEE Trans Instrum Meas 14(6):649\u2013659","journal-title":"IEEE Trans Instrum Meas"},{"issue":"7","key":"718_CR14","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1109\/LGRS.2015.2409112","volume":"12","author":"J Xia","year":"2015","unstructured":"Xia J, Liao W, Chanussot J, Du P, Song G, Philips W (2015) Improving random forest with ensemble of features and semi supervised feature extraction. IEEE Geosci Remote Sens Lett 12(7):1471\u20131475","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"4","key":"718_CR15","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1109\/TNB.2015.2431292","volume":"14","author":"HL\n Dai","year":"2015","unstructured":"Dai HL (2015) Imbalanced protein data classification using ensemble FTM-SVM. IEEE Trans Nanobiosci 14(4):350\u2013359","journal-title":"IEEE Trans Nanobiosci"},{"issue":"10","key":"718_CR16","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1109\/34.58871","volume":"12","author":"LK Hansen","year":"1990","unstructured":"Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12(10):993\u20131001","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"718_CR17","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1109\/TNB.2015.2431292","volume":"14","author":"HL Dai","year":"2015","unstructured":"Dai HL (2015) Imbalanced protein data classification using ensemble FTM-SVM. IEEE Trans Nanobiosci 14(4):350\u2013359","journal-title":"IEEE Trans Nanobiosci"},{"issue":"1","key":"718_CR18","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/TASLP.2014.2367814","volume":"23","author":"H Phan","year":"2015","unstructured":"Phan H, Maab M, Mazur R, Mertins A (2015) Random regression forests for acoustic event detection and classification. IEEE\/ACM Trans Audio Speech Lang Process 23(1):20\u201331","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"issue":"3","key":"718_CR19","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1109\/TCBB.2013.65","volume":"10","author":"A Dehzangi","year":"2013","unstructured":"Dehzangi A (2013) A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem. IEEE\/ACM Trans Comput Biol Bioinf 10(3):564\u2013575","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"718_CR20","unstructured":"Heaton J (2008) Introduction to neural networks with Java Heaton Res Inc"},{"issue":"6","key":"718_CR21","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1109\/TNB.2015.2450233","volume":"14","author":"L Wei","year":"2015","unstructured":"Wei L, Liao M, Gao X, Zou Q (2015) Enhanced protein fold prediction method through a novel feature extraction technique. IEEE Trans Nanobiosci 14(6):649\u2013659","journal-title":"IEEE Trans Nanobiosci"},{"issue":"5","key":"718_CR22","doi-asserted-by":"publisher","first-page":"1638","DOI":"10.1109\/TFUZZ.2014.2371479","volume":"23","author":"Xizhao Wang","year":"2015","unstructured":"Wang Xizhao, Xing Hong-Jie, Li Yan et al (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638\u20131654","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"718_CR23","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.3233\/IFS-151729","volume":"29","author":"Xizhao Wang","year":"2015","unstructured":"Wang Xizhao, Aamir Rana, Ai-Min Fu (2015) Fuzziness based sample categorization for classifier performance improvement. J Intell Fuzzy Syst 29:1185\u20131196","journal-title":"J Intell Fuzzy Syst"},{"key":"718_CR24","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.ins.2016.04.019","volume":"378","author":"RAR Ashfaq","year":"2017","unstructured":"Ashfaq RAR et al, Wang XZ, Huang JZ, Abbas H, He YL (2017) Fuzziness based semi-supervised learning approach for intrusion detection system. Inf Sci 378:484\u2013497","journal-title":"Inf Sci"},{"key":"718_CR25","doi-asserted-by":"crossref","unstructured":"Xizhao Wang, Tianlun Zhang, Ran Wang (2017) Non-iterative deep learning: incorporating restricted Boltzmann machine into multilayer random weight neural networks. IEEE Trans Syst Man Cybern Syst. doi:10.1109\/TSMC.2017.2701419","DOI":"10.1109\/TSMC.2017.2701419"},{"key":"718_CR26","unstructured":"Rulequest: data mining with cubist. www.rulequest.com\/cubist-info.html"},{"key":"718_CR27","unstructured":"Documentation on Xgboost. \n                    https:\/\/goo.gl\/7nttEF"},{"issue":"3","key":"718_CR28","first-page":"1822","volume":"2","author":"A Liam","year":"2002","unstructured":"Liam A, Wiener M (2002) Classification and regression by randomForest. News 2(3):1822","journal-title":"News"},{"key":"718_CR29","unstructured":"K-fold validation, website: scikit-learn. \n                    http:\/\/goo.gl\/JXknN8"},{"key":"718_CR30","unstructured":"XgBoost website: CRAN.R-Project. \n                    http:\/\/goo.gl\/ulWSI3"},{"key":"718_CR31","unstructured":"CART website: CRAN.R-Project. \n                    http:\/\/goo.gl\/ulWSI3"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-017-0718-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-017-0718-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-017-0718-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T12:29:50Z","timestamp":1556195390000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-017-0718-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,22]]},"references-count":31,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,2]]}},"alternative-id":["718"],"URL":"https:\/\/doi.org\/10.1007\/s13042-017-0718-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2017,8,22]]},"assertion":[{"value":"3 October 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}