{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:03:02Z","timestamp":1778601782785,"version":"3.51.4"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2018,4,2]],"date-time":"2018-04-02T00:00:00Z","timestamp":1522627200000},"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 & Applic"],"published-print":{"date-parts":[[2019,10]]},"DOI":"10.1007\/s00521-018-3469-2","type":"journal-article","created":{"date-parts":[[2018,4,2]],"date-time":"2018-04-02T16:40:32Z","timestamp":1522687232000},"page":"6345-6363","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["CBR-PSO: cost-based rough particle swarm optimization approach for high-dimensional imbalanced problems"],"prefix":"10.1007","volume":"31","author":[{"given":"Emel K\u0131z\u0131lkaya","family":"Aydogan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mihrimah","family":"Ozmen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Y\u0131lmaz","family":"Delice","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,4,2]]},"reference":[{"issue":"04","key":"3469_CR1","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1142\/S0219622006002258","volume":"5","author":"Q Yang","year":"2006","unstructured":"Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Mak 5(04):597\u2013604","journal-title":"Int J Inf Technol Decis Mak"},{"key":"3469_CR2","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.ins.2013.07.007","volume":"250","author":"V L\u00f3pez","year":"2013","unstructured":"L\u00f3pez V, Fern\u00e1ndez A, Garc\u00eda S, Palade V, Herrera F (2013) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 250:113\u2013141","journal-title":"Inf Sci"},{"key":"3469_CR3","doi-asserted-by":"crossref","unstructured":"Hu Q, Zhao H, Xie Z, Yu D (2007) Consistency based attribute reduction. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, pp 96\u2013107","DOI":"10.1007\/978-3-540-71701-0_12"},{"issue":"22","key":"3469_CR4","doi-asserted-by":"publisher","first-page":"4928","DOI":"10.1016\/j.ins.2011.07.010","volume":"181","author":"F Min","year":"2011","unstructured":"Min F, He H, Qian Y, Zhu W (2011) Test-cost-sensitive attribute reduction. Inf Sci 181(22):4928\u20134942","journal-title":"Inf Sci"},{"key":"3469_CR5","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.ins.2012.04.031","volume":"211","author":"F Min","year":"2012","unstructured":"Min F, Zhu W (2012) Attribute reduction of data with error ranges and test costs. Inf Sci 211:48\u201367","journal-title":"Inf Sci"},{"issue":"9","key":"3469_CR6","doi-asserted-by":"publisher","first-page":"2237","DOI":"10.1016\/j.ins.2008.01.007","volume":"178","author":"C Wang","year":"2008","unstructured":"Wang C, Wu C, Chen D (2008) A systematic study on attribute reduction with rough sets based on general binary relations. Inf Sci 178(9):2237\u20132261","journal-title":"Inf Sci"},{"issue":"17","key":"3469_CR7","doi-asserted-by":"publisher","first-page":"3356","DOI":"10.1016\/j.ins.2008.05.010","volume":"178","author":"Y Yao","year":"2008","unstructured":"Yao Y, Zhao Y (2008) Attribute reduction in decision-theoretic rough set models. Inf Sci 178(17):3356\u20133373","journal-title":"Inf Sci"},{"issue":"9","key":"3469_CR8","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1002\/int.10128","volume":"18","author":"WX Zhang","year":"2003","unstructured":"Zhang WX, Mi JS, Wu WZ (2003) Approaches to knowledge reductions in inconsistent systems. Int J Intell Syst 18(9):989\u20131000","journal-title":"Int J Intell Syst"},{"key":"3469_CR9","doi-asserted-by":"crossref","unstructured":"Zhao Y, Wong SM, Yao Y (2011) A note on attribute reduction in the decision-theoretic rough set model. In: Transactions on rough sets XIII. Springer, Berlin, pp 260\u2013275","DOI":"10.1007\/978-3-642-18302-7_14"},{"key":"3469_CR10","doi-asserted-by":"crossref","unstructured":"Zhou X, Li H (2009) A multi-view decision model based on decision-theoretic rough set. In: International conference on rough sets and knowledge technology. Springer, Berlin, pp. 650\u2013657","DOI":"10.1007\/978-3-642-02962-2_82"},{"key":"3469_CR11","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.ins.2012.07.010","volume":"219","author":"X Jia","year":"2013","unstructured":"Jia X, Liao W, Tang Z, Shang L (2013) Minimum cost attribute reduction in decision-theoretic rough set models. Inf Sci 219:151\u2013167","journal-title":"Inf Sci"},{"issue":"2","key":"3469_CR12","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1016\/j.asoc.2011.10.010","volume":"12","author":"EK Aydogan","year":"2012","unstructured":"Aydogan EK, Karaoglan I, Pardalos PM (2012) hGA: hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems. Appl Soft Comput 12(2):800\u2013806","journal-title":"Appl Soft Comput"},{"issue":"8","key":"3469_CR13","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1016\/j.ins.2009.12.020","volume":"180","author":"FJ Berlanga","year":"2010","unstructured":"Berlanga FJ, Rivera AJ, del Jes\u00fas MJ, Herrera F (2010) GP-COACH: genetic programming-based learning of compact and accurate fuzzy rule-based classification systems for high-dimensional problems. Inf Sci 180(8):1183\u20131200","journal-title":"Inf Sci"},{"issue":"3","key":"3469_CR14","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1111\/j.1467-8640.2010.00358.x","volume":"26","author":"ZH Zhou","year":"2010","unstructured":"Zhou ZH, Liu XY (2010) On multi-class cost-sensitive learning. Comput Intell 26(3):232\u2013257","journal-title":"Comput Intell"},{"issue":"2","key":"3469_CR15","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1214\/aos\/1028144844","volume":"26","author":"T Hastie","year":"1998","unstructured":"Hastie T, Tibshirani R (1998) Classification by pairwise coupling. Ann Stat 26(2):451\u2013471","journal-title":"Ann Stat"},{"key":"3469_CR16","first-page":"101","volume":"5","author":"R Rifkin","year":"2004","unstructured":"Rifkin R, Klautau A (2004) In defense of one-vs-all classification. J Mach Learn Res 5:101\u2013141","journal-title":"J Mach Learn Res"},{"key":"3469_CR17","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.knosys.2013.01.018","volume":"42","author":"A Fern\u00e1Ndez","year":"2013","unstructured":"Fern\u00e1Ndez A, L\u00f3Pez V, Galar M, Del Jesus MJ, Herrera F (2013) Analysing the classification of imbalanced data-sets with multiple classes: binarization techniques and ad-hoc approaches. Knowl Based Syst 42:97\u2013110","journal-title":"Knowl Based Syst"},{"issue":"12","key":"3469_CR18","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1016\/j.patrec.2013.04.019","volume":"34","author":"P Thanathamathee","year":"2013","unstructured":"Thanathamathee P, Lursinsap C (2013) Handling imbalanced datasets with synthetic boundary data generation using bootstrap re-sampling and AdaBoost techniques. Pattern Recognit Lett 34(12):1339\u20131347","journal-title":"Pattern Recognit Lett"},{"key":"3469_CR19","doi-asserted-by":"crossref","unstructured":"McCarthy K, Zabar B, Weiss G (2005) Does cost-sensitive learning beat sampling for classifying rare classes?. In: Proceedings of the 1st international workshop on Utility-based data mining. ACM, pp 69\u201377","DOI":"10.1145\/1089827.1089836"},{"key":"3469_CR20","unstructured":"Liu W, Chawla S, Cieslak DA, Chawla NV (2010) A robust decision tree algorithm for imbalanced datasets. In: SDM, vol 10, pp 766\u2013777"},{"issue":"8","key":"3469_CR21","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1016\/j.knosys.2008.03.031","volume":"21","author":"J Liu","year":"2008","unstructured":"Liu J, Hu Q, Yu D (2008) A comparative study on rough set based class-imbalance learning. Knowl Based Syst 21(8):753\u2013763","journal-title":"Knowl Based Syst"},{"issue":"1","key":"3469_CR22","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s10596-009-9143-0","volume":"14","author":"S Sinha","year":"2010","unstructured":"Sinha S, Singh TN, Singh VK, Verma AK (2010) Epoch determination for neural network by self-organized map (SOM). Comput Geosci 14(1):199\u2013206","journal-title":"Comput Geosci"},{"issue":"1","key":"3469_CR23","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/TNN.2006.882812","volume":"18","author":"X Hong","year":"2007","unstructured":"Hong X, Chen S, Harris CJ (2007) A kernel-based two-class classifier for imbalanced datasets. IEEE Trans Neural Netw 18(1):28\u201341","journal-title":"IEEE Trans Neural Netw"},{"issue":"4","key":"3469_CR24","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"M Galar","year":"2012","unstructured":"Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2012) A review on ensembles for the class-imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(4):463\u2013484","journal-title":"IEEE Trans Syst Man Cybern Part C (Appl Rev)"},{"key":"3469_CR25","doi-asserted-by":"crossref","unstructured":"Ertekin S, Huang J, Giles CL (2007) Active learning for class-imbalance problem. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 823\u2013824","DOI":"10.1145\/1277741.1277927"},{"key":"3469_CR26","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"issue":"8","key":"3469_CR27","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1016\/j.patcog.2011.01.017","volume":"44","author":"M Galar","year":"2011","unstructured":"Galar M, Fern\u00e1ndez A, Barrenechea E, Bustince H, Herrera F (2011) An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recognit 44(8):1761\u20131776","journal-title":"Pattern Recognit"},{"key":"3469_CR28","doi-asserted-by":"crossref","unstructured":"Elkan C, Noto K (2008) Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 213\u2013220","DOI":"10.1145\/1401890.1401920"},{"key":"3469_CR29","doi-asserted-by":"crossref","unstructured":"Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) SMOTEBoost: improving prediction of the minority class in boosting. In: European conference on principles of data mining and knowledge discovery. Springer, Berlin, pp 107\u2013119","DOI":"10.1007\/978-3-540-39804-2_12"},{"key":"3469_CR30","doi-asserted-by":"crossref","unstructured":"Rout N, Mishra D, Mallick MK (2018) Handling imbalanced data: a survey. In: International proceedings on advances in soft computing, intelligent systems and applications. Springer, Singapore, pp 431\u2013443","DOI":"10.1007\/978-981-10-5272-9_39"},{"key":"3469_CR31","doi-asserted-by":"crossref","unstructured":"Domingos P (1999) Metacost: a general method for making classifiers cost-sensitive. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 155\u2013164","DOI":"10.1145\/312129.312220"},{"issue":"1","key":"3469_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00366-011-0210-5","volume":"28","author":"TN Singh","year":"2012","unstructured":"Singh TN, Verma AK (2012) Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks. Eng Comput 28(1):1\u201312","journal-title":"Eng Comput"},{"key":"3469_CR33","unstructured":"Fan W, Stolfo SJ, Zhang J, Chan PK (1999) AdaCost: misclassification cost-sensitive boosting. In: Icml, pp 97\u2013105"},{"key":"3469_CR34","doi-asserted-by":"crossref","unstructured":"Joshi MV, Kumar V, Agarwal RC (2001) Evaluating boosting algorithms to classify rare classes: comparison and improvements. In: Proceedings IEEE international conference on data mining, 2001. ICDM 2001. IEEE, pp 257\u2013264","DOI":"10.1109\/ICDM.2001.989527"},{"key":"3469_CR35","doi-asserted-by":"crossref","unstructured":"Hu S, Liang Y, Ma L, He Y (2009) MSMOTE: improving classification performance when training data is imbalanced. In\u00a0Proceedings of the 2009 Second international workshop on computer science and engineering, vol 2, pp 13\u201317","DOI":"10.1109\/WCSE.2009.756"},{"key":"3469_CR36","doi-asserted-by":"crossref","unstructured":"Han H, Wang WY, Mao BH (2005) Borderline-SMOTE: a new over-sampling method in imbalanced datasets learning. In: International conference on intelligent computing. Springer, Berlin, pp 878\u2013887","DOI":"10.1007\/11538059_91"},{"key":"3469_CR37","unstructured":"He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In:\u00a02008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence). IEEE, pp 1322\u20131328"},{"key":"3469_CR38","unstructured":"Kubat M, Matwin S (1997). Addressing the curse of imbalanced training sets: one-sided selection. In: ICML, vol 97, pp 179\u2013186"},{"issue":"1","key":"3469_CR39","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"GE Batista","year":"2004","unstructured":"Batista GE, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM Sigkdd Explor Newsl 6(1):20\u201329","journal-title":"ACM Sigkdd Explor Newsl"},{"key":"3469_CR40","doi-asserted-by":"crossref","unstructured":"Stefanowski J, Wilk S (2008) Selective pre-processing of imbalanced data for improving classification performance. In: International conference on data warehousing and knowledge discovery. Springer, Berlin, pp 283\u2013292","DOI":"10.1007\/978-3-540-85836-2_27"},{"key":"3469_CR41","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.measurement.2016.03.050","volume":"88","author":"N Madhubabu","year":"2016","unstructured":"Madhubabu N, Singh PK, Kainthola A, Mahanta B, Tripathy A, Singh TN (2016) Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement 88:202\u2013213","journal-title":"Measurement"},{"key":"3469_CR42","unstructured":"Elkan C (2001b) The foundations of cost-sensitive learning. In: International joint conference on artificial intelligence, vol 17, no. 1. Lawrence Erlbaum Associates Ltd, pp 973\u2013978"},{"issue":"3","key":"3469_CR43","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1109\/TKDE.2002.1000348","volume":"14","author":"KM Ting","year":"2002","unstructured":"Ting KM (2002) An instance-weighting method to induce cost-sensitive trees. IEEE Trans Knowl Data Eng 14(3):659\u2013665","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3469_CR44","unstructured":"Drummond C, Holte RC (2003) C4. 5, class-imbalance, and cost sensitivity: why under-sampling beats over-sampling. In:\u00a0Workshop on learning from imbalanced datasets II, vol 11"},{"key":"3469_CR45","unstructured":"Maloof MA (2003) Learning when datasets are imbalanced and when costs are unequal and unknown. In: ICML-2003 workshop on learning from imbalanced datasets II, vol 2, pp 2\u20131"},{"issue":"1","key":"3469_CR46","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/TKDE.2006.17","volume":"18","author":"ZH Zhou","year":"2006","unstructured":"Zhou ZH, Liu XY (2006) Training cost-sensitive neural networks with methods addressing the class-imbalance problem. IEEE Trans Knowl Data Eng 18(1):63\u201377","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"Dec","key":"3469_CR47","first-page":"113","volume":"1","author":"EL Allwein","year":"2000","unstructured":"Allwein EL, Schapire RE, Singer Y (2000) Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res 1(Dec):113\u2013141","journal-title":"J Mach Learn Res"},{"issue":"4","key":"3469_CR48","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1007\/s10596-008-9088-8","volume":"12","author":"D Ramakrishnan","year":"2008","unstructured":"Ramakrishnan D, Singh TN, Purwar N, Barde KS, Gulati A, Gupta S (2008) Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data, Gujarat, India. Comput Geosci 12(4):491\u2013501","journal-title":"Comput Geosci"},{"issue":"1","key":"3469_CR49","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1016\/j.asoc.2010.02.014","volume":"11","author":"G Haixiang","year":"2011","unstructured":"Haixiang G, Xiuwu L, Kejun Z, Chang D, Yanhui G (2011) Optimizing reservoir features in oil exploration management based on fusion of soft computing. Appl Soft Comput 11(1):1144\u20131155","journal-title":"Appl Soft Comput"},{"issue":"8","key":"3469_CR50","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861\u2013874","journal-title":"Pattern Recognit Lett"},{"issue":"2","key":"3469_CR51","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1023\/A:1010920819831","volume":"45","author":"DJ Hand","year":"2001","unstructured":"Hand DJ, Till RJ (2001) A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn 45(2):171\u2013186","journal-title":"Mach Learn"},{"issue":"1","key":"3469_CR52","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1504\/IJDATS.2008.020020","volume":"1","author":"D Anil Kumar","year":"2008","unstructured":"Anil Kumar D, Ravi V (2008) Predicting credit card customer churn in banks using data mining. Int J Data Anal Techn Strat 1(1):4\u201328","journal-title":"Int J Data Anal Techn Strat"},{"issue":"1","key":"3469_CR53","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1038\/ng1060","volume":"33","author":"S Ramaswamy","year":"2003","unstructured":"Ramaswamy S, Ross KN, Lander ES, Golub TR (2003) A molecular signature of metastasis in primary solid tumors. Nat Genet 33(1):49\u201354","journal-title":"Nat Genet"},{"issue":"5","key":"3469_CR54","doi-asserted-by":"publisher","first-page":"9072","DOI":"10.1016\/j.eswa.2008.12.037","volume":"36","author":"JT Horng","year":"2009","unstructured":"Horng JT, Wu LC, Liu BJ, Kuo JL, Kuo WH, Zhang JJ (2009) An expert system to classify microarray gene expression data using gene selection by decision tree. Expert Syst Appl 36(5):9072\u20139081","journal-title":"Expert Syst Appl"},{"issue":"1","key":"3469_CR55","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1145\/1007730.1007741","volume":"6","author":"Z Zheng","year":"2004","unstructured":"Zheng Z, Wu X, Srihari R (2004) Feature selection for text categorization on imbalanced data. ACM Sigkdd Explor Newsl 6(1):80\u201389","journal-title":"ACM Sigkdd Explor Newsl"},{"issue":"1","key":"3469_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2006.04.001","volume":"33","author":"W Shang","year":"2007","unstructured":"Shang W, Huang H, Zhu H, Lin Y, Qu Y, Wang Z (2007) A novel feature selection algorithm for text categorization. Expert Syst Appl 33(1):1\u20135","journal-title":"Expert Syst Appl"},{"issue":"1","key":"3469_CR57","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.eswa.2007.06.028","volume":"35","author":"S Tan","year":"2008","unstructured":"Tan S (2008) An improved centroid classifier for text categorization. Expert Syst Appl 35(1):279\u2013285","journal-title":"Expert Syst Appl"},{"key":"3469_CR58","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.ins.2011.01.026","volume":"229","author":"BJ Park","year":"2013","unstructured":"Park BJ, Oh SK, Pedrycz W (2013) The design of polynomial function-based neural network predictors for detection of software defects. Inf Sci 229:40\u201357","journal-title":"Inf Sci"},{"issue":"9361","key":"3469_CR59","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1016\/S0140-6736(03)12775-4","volume":"361","author":"N Iizuka","year":"2003","unstructured":"Iizuka N, Oka M, Yamada-Okabe H, Nishida M, Maeda Y, Mori N, Takao T, Tamesa T, Tangoku A, Tabuchi H, Hamada K (2003) Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection. The lancet 361(9361):923\u2013929","journal-title":"The lancet"},{"issue":"Mar","key":"3469_CR60","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(Mar):1157\u20131182","journal-title":"J Mach Learn Res"},{"issue":"9","key":"3469_CR61","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1046\/j.1464-410X.2003.04230.x","volume":"91","author":"A Hamid","year":"2003","unstructured":"Hamid A, Dwivedi US, Singh TN, Gopi Kishore M, Mahmood M, Singh H, Tandon V, Singh PB (2003) Artificial neural networks in predicting optimum renal stone fragmentation by extracorporeal shock wave lithotripsy: a preliminary study. BJU Int 91(9):821\u2013824","journal-title":"BJU Int"},{"key":"3469_CR62","unstructured":"Pawlak, Z. (1991). Rough sets: theoretical aspects of reasoning about data, system theory. Knowl Eng Probl Solving, vol 9"},{"issue":"5","key":"3469_CR63","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1097\/00007632-199903010-00021","volume":"24","author":"JD Lurie","year":"1999","unstructured":"Lurie JD, Sox HC (1999) Principles of medical decision making. Spine 24(5):493\u2013498","journal-title":"Spine"},{"key":"3469_CR64","volume-title":"Social judgment: assimilation and contrast effects in communication and attitude change","author":"M Sherif","year":"1961","unstructured":"Sherif M, Hovland CI (1961) Social judgment: assimilation and contrast effects in communication and attitude change. YALE University Press, New Haven, CT"},{"key":"3469_CR65","unstructured":"Ali K, Manganaris S, Srikant R (1997) Partial classification using association rules. In: KDD, vol 97, pp p115\u2013118)"},{"issue":"1","key":"3469_CR66","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/S0020-7373(88)80032-4","volume":"29","author":"Z Pawlak","year":"1988","unstructured":"Pawlak Z, Wong SKM, Ziarko W (1988) Rough sets: probabilistic versus deterministic approach. Int J Man Mach Stud 29(1):81\u201395","journal-title":"Int J Man Mach Stud"},{"key":"3469_CR67","unstructured":"Yao YY (1990) Wong. SKM, Lingras. P.: A decision-theoretic rough set model. In: Proceeding of ISMIS, pp 17\u201325"},{"key":"3469_CR68","volume-title":"Pattern classification and scene analysis","author":"RO Duda","year":"1973","unstructured":"Duda RO, Hart PE (1973) Pattern classification and scene analysis, vol 3. Wiley, New York"},{"issue":"6","key":"3469_CR69","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1016\/j.ins.2010.11.019","volume":"181","author":"Y Yao","year":"2011","unstructured":"Yao Y (2011) The superiority of three-way decisions in probabilistic rough set models. Inf Sci 181(6):1080\u20131096","journal-title":"Inf Sci"},{"issue":"1","key":"3469_CR70","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s10994-008-5083-5","volume":"74","author":"Y Yang","year":"2009","unstructured":"Yang Y, Webb GI (2009) Discretization for naive-Bayes learning: managing discretization bias and variance. Mach Learn 74(1):39\u201374","journal-title":"Mach Learn"},{"issue":"5","key":"3469_CR71","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/j.parco.2003.12.015","volume":"30","author":"T Sousa","year":"2004","unstructured":"Sousa T, Silva A, Neves A (2004) Particle swarm based data mining algorithms for classification tasks. Parallel Comput 30(5):767\u2013783","journal-title":"Parallel Comput"},{"issue":"3","key":"3469_CR72","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1016\/j.asoc.2005.09.004","volume":"7","author":"I Falco De","year":"2007","unstructured":"De Falco I, Della Cioppa A, Tarantino E (2007) Facing classification problems with particle swarm optimization. Appl Soft Comput 7(3):652\u2013658","journal-title":"Appl Soft Comput"},{"key":"3469_CR73","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart RC (1997). A discrete binary version of the particle swarm algorithm. In: Systems, man, and cybernetics, 1997. Computational cybernetics and simulation., 1997 IEEE international conference on, vol 5. IEEE, pp 4104\u20134108","DOI":"10.1109\/ICSMC.1997.637339"},{"issue":"2","key":"3469_CR74","first-page":"1","volume":"5","author":"MF Ta\u015fgetiren","year":"2003","unstructured":"Ta\u015fgetiren MF, Liang YC (2003) A binary particle swarm optimization algorithm for lot sizing problem. J Econ Soc Res 5(2):1\u201320","journal-title":"J Econ Soc Res"},{"key":"3469_CR75","unstructured":"Arizona State University. Feature selection datasets. \n                    http:\/\/featureselection.asu.edu\/datasets.php"},{"key":"3469_CR76","unstructured":"Statnikov A, Aliferis C, Tsardinos I. Gems: gene expression model selector. \n                    http:\/\/www.gems-system.org\/"},{"issue":"7","key":"3469_CR77","first-page":"1602","volume":"63","author":"CL Nutt","year":"2003","unstructured":"Nutt CL, Mani DR, Betensky RA, Tamayo P, Cairncross JG, Ladd C, Pohl U, Hartmann C, McLaughlin ME, Batchelor TT, Black PM (2003) Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Can Res 63(7):1602\u20131607","journal-title":"Can Res"},{"key":"3469_CR78","volume-title":"C4.5: Programs for machine learning","author":"JR Quinlan","year":"1993","unstructured":"Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kauffman, Burlington"},{"key":"3469_CR79","volume-title":"Pattern classification","author":"RO Duda","year":"2001","unstructured":"Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. John Wiley, New York","edition":"2"},{"key":"3469_CR80","volume-title":"Discriminant analysis and statistical pattern recognition","author":"GJ McLachlan","year":"2004","unstructured":"McLachlan GJ (2004) Discriminant analysis and statistical pattern recognition. Wiley, Hoboken"},{"key":"3469_CR81","unstructured":"Roffo G (2016) Feature selection library (MATLAB Toolbox). arXiv preprint \n                    arXiv:1607.01327"},{"issue":"1","key":"3469_CR82","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11(1):10\u201318","journal-title":"SIGKDD Explor"},{"issue":"1","key":"3469_CR83","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","volume":"40","author":"C Seiffert","year":"2010","unstructured":"Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A (2010) RUSBoost: a hybrid approach to alleviating class-imbalance. IEEE Trans Syst Man Cybern A 40(1):185\u2013197","journal-title":"IEEE Trans Syst Man Cybern A"},{"key":"3469_CR84","unstructured":"http:\/\/www.keel.es"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3469-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-018-3469-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3469-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,20]],"date-time":"2019-10-20T20:45:12Z","timestamp":1571604312000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-018-3469-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,2]]},"references-count":84,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2019,10]]}},"alternative-id":["3469"],"URL":"https:\/\/doi.org\/10.1007\/s00521-018-3469-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,2]]},"assertion":[{"value":"19 October 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}