{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:53:18Z","timestamp":1768823598666,"version":"3.49.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2015,9,22]],"date-time":"2015-09-22T00:00:00Z","timestamp":1442880000000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2016,11]]},"DOI":"10.1007\/s00521-015-2064-z","type":"journal-article","created":{"date-parts":[[2015,9,22]],"date-time":"2015-09-22T06:22:06Z","timestamp":1442902926000},"page":"2279-2288","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A fine-grained Random Forests using class decomposition: an application to medical diagnosis"],"prefix":"10.1007","volume":"27","author":[{"given":"Eyad","family":"Elyan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed Medhat","family":"Gaber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,9,22]]},"reference":[{"key":"2064_CR1","doi-asserted-by":"crossref","unstructured":"Abdallah ZS, Gaber MM (2011) Kb-cb-n classification: towards unsupervised approach for supervised learning. In: Computational intelligence and data mining (CIDM), 2011 IEEE symposium on IEEE, pp 283\u2013290","DOI":"10.1109\/CIDM.2011.5949435"},{"key":"2064_CR2","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.neucom.2014.09.074","volume":"150","author":"ZS Abdallah","year":"2015","unstructured":"Abdallah ZS, Gaber MM, Srinivasan B, Krishnaswamy S (2015) Adaptive mobile activity recognition system with evolving data streams. Neurocomputing 150:304\u2013317","journal-title":"Neurocomputing"},{"issue":"18","key":"2064_CR3","doi-asserted-by":"crossref","first-page":"2010","DOI":"10.1093\/bioinformatics\/btn356","volume":"24","author":"D Amaratunga","year":"2008","unstructured":"Amaratunga D, Cabrera J, Lee Y-S (2008) Enriched random forests. Bioinformatics 24(18):2010\u20132014","journal-title":"Bioinformatics"},{"issue":"7","key":"2064_CR4","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1162\/neco.1997.9.7.1545","volume":"9","author":"Y Amit","year":"1997","unstructured":"Amit Y, Geman D (1997) Shape quantization and recognition with randomized trees. Neural Comput. 9(7):1545\u20131588","journal-title":"Neural Comput."},{"key":"2064_CR5","doi-asserted-by":"crossref","unstructured":"Bader-El-Den M, Gaber M (2012) Garf: towards self-optimised random forests. In: Neural information processing. Springer, pp 506\u2013515","DOI":"10.1007\/978-3-642-34481-7_62"},{"issue":"2","key":"2064_CR6","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach. Learn. 24(2):123\u2013140","journal-title":"Mach. Learn."},{"issue":"1","key":"2064_CR7","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach. Learn. 45(1):5\u201332","journal-title":"Mach. Learn."},{"key":"2064_CR8","unstructured":"Dietterich TG, Bakiri G (1991) Error-correcting output codes: a general method for improving multiclass inductive learning programs. In: AAAI, Citeseer, pp 572\u2013577"},{"issue":"6","key":"2064_CR9","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1162\/neco.1994.6.6.1289","volume":"6","author":"H Drucker","year":"1994","unstructured":"Drucker H, Cortes C, Jackel LD, LeCun Y, Vapnik V (1994) Boosting and other ensemble methods. Neural Comput. 6(6):1289\u20131301","journal-title":"Neural Comput."},{"key":"2064_CR10","doi-asserted-by":"crossref","first-page":"4164","DOI":"10.1118\/1.2786864","volume":"34","author":"M Elter","year":"2007","unstructured":"Elter M, Schulz-Wendtland R, Wittenberg T (2007) The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. Med. Phys. 34:4164","journal-title":"Med. Phys."},{"key":"2064_CR11","doi-asserted-by":"crossref","unstructured":"Fawagreh K, Gaber MM, Elyan E (2014) Diversified random forests using random subspaces. In: Intelligent data engineering and automated learning\u2013IDEAL 2014. Springer, pp 85\u201392","DOI":"10.1007\/978-3-319-10840-7_11"},{"issue":"1","key":"2064_CR12","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1080\/21642583.2014.956265","volume":"2","author":"K Fawagreh","year":"2014","unstructured":"Fawagreh K, Gaber MM, Elyan E (2014) Random forests: from early developments to recent advancements. Syst Sci Control Eng Open Access J 2(1):602\u2013609","journal-title":"Syst Sci Control Eng Open Access J"},{"key":"2064_CR13","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15:3133\u20133181","journal-title":"J Mach Learn Res"},{"issue":"2","key":"2064_CR14","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1006\/inco.1995.1136","volume":"121","author":"Y Freund","year":"1995","unstructured":"Freund Y (1995) Boosting a weak learning algorithm by majority. Inf Comput 121(2):256\u2013285","journal-title":"Inf Comput"},{"issue":"4","key":"2064_CR15","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","volume":"38","author":"JH Friedman","year":"2002","unstructured":"Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367\u2013378","journal-title":"Comput Stat Data Anal"},{"key":"2064_CR16","doi-asserted-by":"crossref","unstructured":"Ho TK (1995) Random decision forests. In: Proceedings of the third international conference on document analysis and recognition, vol 1, pp 278\u2013282","DOI":"10.1109\/ICDAR.1995.598994"},{"issue":"8","key":"2064_CR17","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","volume":"20","author":"TK Ho","year":"1998","unstructured":"Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832\u2013844","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"2064_CR18","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/0031-3203(91)90074-F","volume":"24","author":"Z-Q Hong","year":"1991","unstructured":"Hong Z-Q, Yang J-Y (1991) Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognit 24(4):317\u2013324","journal-title":"Pattern Recognit"},{"key":"2064_CR19","volume-title":"Algorithms for clustering data","author":"AK Jain","year":"1988","unstructured":"Jain AK, Dubes RC et al (1988) Algorithms for clustering data, vol 6. Prentice hall, Englewood Cliffs"},{"key":"2064_CR20","doi-asserted-by":"crossref","unstructured":"Latinne P, Debeir O, Decaestecker C (2001) Limiting the number of trees in random forests. In: Multiple classifier systems. Springer, pp 178\u2013187","DOI":"10.1007\/3-540-48219-9_18"},{"issue":"3","key":"2064_CR21","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M (2002) Classification and regression by randomforest. R News 2(3):18\u201322","journal-title":"R News"},{"key":"2064_CR22","unstructured":"Lichman M (2013) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA. http:\/\/archive.ics.uci.edu\/ml"},{"issue":"1","key":"2064_CR23","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/1475-925X-6-23","volume":"6","author":"MA Little","year":"2007","unstructured":"Little MA, McSharry PE, Roberts SJ, Costello DA, Moroz IM (2007) Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMed Eng OnLine 6(1):23. http:\/\/doi.org\/10.1186\/1475-925X-6-23","journal-title":"BioMed Eng OnLine"},{"key":"2064_CR24","unstructured":"MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth berkeley symposium on math, statistics, and probability, vol 1, pp 281\u2013297"},{"key":"2064_CR25","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1287\/opre.43.4.570","volume":"43","author":"OL Mangasarian","year":"1995","unstructured":"Mangasarian OL, Street WN, Wolberg WH (1995) breast cancer diagnosis and prognosis via linear programming. Oper Res 43:570\u2013577","journal-title":"Oper Res"},{"key":"2064_CR26","unstructured":"Polaka I (2013) Clustering algorithm specifics in class decomposition. In: International conference on applied information and communication technologies (AICT2013), 25\u201326 April 2013, Jelgava, Latvia"},{"issue":"3","key":"2064_CR27","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","volume":"6","author":"R Polikar","year":"2006","unstructured":"Polikar R (2006) Ensemble based systems in decision making. IEEE Circuits Syst Mag 6(3):21\u201345","journal-title":"IEEE Circuits Syst Mag"},{"key":"2064_CR28","unstructured":"Repository U (1996) Heart Disease dataset. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Statlog+(Heart) . Accessed Dec 2014"},{"key":"2064_CR29","doi-asserted-by":"crossref","unstructured":"Robnik-\u0160ikonja M (2004) Improving random forests. In: Machine learning: ECML 2004. Springer, pp 359\u2013370","DOI":"10.1007\/978-3-540-30115-8_34"},{"key":"2064_CR30","doi-asserted-by":"crossref","unstructured":"Tsymbal A, Pechenizkiy M, Cunningham P (2006) Dynamic integration with random forests. In: Machine learning: ECML 2006. Springer, pp 801\u2013808","DOI":"10.1007\/11871842_82"},{"key":"2064_CR31","doi-asserted-by":"crossref","unstructured":"Vilalta R, Achari M-K, Eick CF (2003) Class decomposition via clustering: a new framework for low-variance classifiers. In: Data mining, 2003, ICDM 2003, third IEEE international conference on IEEE, pp 673\u2013676","DOI":"10.1109\/ICDM.2003.1251005"},{"issue":"2","key":"2064_CR32","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241\u2013259","journal-title":"Neural Netw"},{"key":"2064_CR33","doi-asserted-by":"publisher","unstructured":"Woolson RF (2008) Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials, pp 1\u20133. doi: 10.1002\/9780471462422.eoct979","DOI":"10.1002\/9780471462422.eoct979"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2064-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-015-2064-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2064-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2064-z","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,29]],"date-time":"2019-05-29T02:21:24Z","timestamp":1559096484000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-015-2064-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,9,22]]},"references-count":33,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2016,11]]}},"alternative-id":["2064"],"URL":"https:\/\/doi.org\/10.1007\/s00521-015-2064-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,9,22]]}}}