{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T22:43:35Z","timestamp":1649112215741},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2012,2,16]],"date-time":"2012-02-16T00:00:00Z","timestamp":1329350400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2012,7]]},"DOI":"10.1007\/s00500-012-0811-y","type":"journal-article","created":{"date-parts":[[2012,2,15]],"date-time":"2012-02-15T05:00:28Z","timestamp":1329282028000},"page":"1267-1286","source":"Crossref","is-referenced-by-count":1,"title":["Clustering-based initialization of Learning Classifier Systems"],"prefix":"10.1007","volume":"16","author":[{"given":"Fani A.","family":"Tzima","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pericles A.","family":"Mitkas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John B.","family":"Theocharis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2012,2,16]]},"reference":[{"issue":"2","key":"811_CR2","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1109\/TSMCB.2002.805696","volume":"33","author":"JS Aguilar-Ruiz","year":"2003","unstructured":"Aguilar-Ruiz JS, Riquelme JC, Toro M (2003) Evolutionary learning of hierarchical decision rules. IEEE Trans Syst Man Cybern B 33(2):324\u2013331","journal-title":"IEEE Trans Syst Man Cybern B"},{"issue":"4","key":"811_CR1","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1109\/TEVC.2006.883466","volume":"11","author":"J Aguilar-Ruiz","year":"2007","unstructured":"Aguilar-Ruiz J, Giraldez R, Riquelme J (2007) Natural encoding for evolutionary supervised learning. IEEE Trans Evol Comput 11(4):466\u2013479. doi: 10.1109\/TEVC.2006.883466","journal-title":"IEEE Trans Evol Comput"},{"key":"811_CR3","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s00500-008-0323-y","volume":"13","author":"J Alcal\u00e1-Fdez","year":"2009","unstructured":"Alcal\u00e1-Fdez J, S\u00e1nchez L, Garc\u00eda S, del Jesus M, Ventura S, Garrell J, Otero J, Romero C, Bacardit J, Rivas V, Fern\u00e1ndez J, Herrera F (2009) Keel: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13:307\u2013318. doi: 10.1007\/s00500-008-0323-y","journal-title":"Soft Comput"},{"key":"811_CR4","unstructured":"Asuncion A, Newman DJ (2010) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine. http:\/\/archive.ics.uci.edu\/ml"},{"key":"811_CR5","unstructured":"Bacardit J (2004) Pittsburgh genetics-based machine learning in the data mining era: representations, generalization, and run-time. PhD thesis, Ramon Llull University, Barcelona, Catalonia, Spain"},{"key":"811_CR6","doi-asserted-by":"crossref","unstructured":"Bacardit J (2005) Analysis of the initialization stage of a Pittsburgh approach learning classifier system. In: Proceedings of the 2005 conference on genetic and evolutionary computation (GECCO \u201905). ACM, New York, pp 1843\u20131850","DOI":"10.1145\/1068009.1068321"},{"issue":"3","key":"811_CR7","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1162\/106365603322365289","volume":"11","author":"E Bernad\u00f3-Mansilla","year":"2003","unstructured":"Bernad\u00f3-Mansilla E, Garrell-Guiu JM (2003) Accuracy-based learning classifier systems: models, analysis and applications to classification tasks. Evol Comput 11(3):209\u2013238. doi: 10.1162\/106365603322365289","journal-title":"Evol Comput"},{"key":"811_CR8","doi-asserted-by":"crossref","unstructured":"Bonelli P, Parodi A, Sen S, Wilson SW (1990) NEWBOOLE: a fast GBML system. In: Proceedings of the 7th international conference on machine learning. Morgan Kaufmann, San Francisco, pp 153\u2013159","DOI":"10.1016\/B978-1-55860-141-3.50022-5"},{"key":"811_CR9","unstructured":"Breiman L (2002) Wald Lecture II\u2014looking inside the black box. In: 277th meeting of the Institute of Mathematical Statistics"},{"key":"811_CR10","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1162\/evco.1998.6.1.81","volume":"6","author":"EK Burke","year":"1998","unstructured":"Burke EK, Newall JP, Weare RF (1998) Initialization strategies and diversity in evolutionary timetabling. Evol Comput 6:81\u2013103","journal-title":"Evol Comput"},{"key":"811_CR11","unstructured":"Butz MV, Wilson SW (2001) An algorithmic description of XCS. In: IWLCS \u201900: revised papers from the third international workshop on advances in learning classifier systems. Springer, London, pp 253\u2013272"},{"issue":"1","key":"811_CR12","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/TEVC.2003.818194","volume":"8","author":"MV Butz","year":"2004","unstructured":"Butz MV, Kovacs T, Lanzi PL, Wilson SW (2004) Toward a theory of generalization and learning in XCS. IEEE Trans Evol Comput 8(1):28\u201346","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"811_CR13","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s10710-005-7619-9","volume":"6","author":"MV Butz","year":"2005","unstructured":"Butz MV, Sastry K, Goldberg DE (2005) Strong, stable, and reliable fitness pressure in XCS due to tournament selection. Genet Program Evol Mach 6(1):53\u201377. doi: 10.1007\/s10710-005-7619-9","journal-title":"Genet Program Evol Mach"},{"key":"811_CR14","unstructured":"Chou CH, Chen JN (2000) Genetic algorithms: initialization schemes and genes extraction. In: The ninth IEEE international conference on fuzzy systems, 2000 (FUZZ IEEE 2000), vol 2, pp 965\u2013968"},{"key":"811_CR15","unstructured":"De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Ann Arbor, MI, USA"},{"key":"811_CR16","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1023\/A:1022617912649","volume":"13","author":"KA De Jong","year":"1993","unstructured":"De Jong KA, Spears WM, Gordon DF (1993) Using genetic algorithms for concept learning. Mach Learn 13:161\u2013188. doi: 10.1007\/BF00993042","journal-title":"Mach Learn"},{"key":"811_CR17","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"811_CR18","doi-asserted-by":"crossref","unstructured":"Dixon PW, Corne DW, Oates MJ (2003) A ruleset reduction algorithm for the XCS learning classifier system. In: Learning classifier systems, 5th international workshop, IWLCS 2002, Granada, Spain, September 7\u20138, 2002, revised papers. Lecture notes in computer science, vol 2661. Springer, Berlin-Heidelberg, pp 20\u201329","DOI":"10.1007\/978-3-540-40029-5_2"},{"key":"811_CR19","unstructured":"Frank E, Witten IH (1998) Generating accurate rule sets without global optimization. In: ICML \u201998: proceedings of the fifteenth international conference on machine learning. Morgan Kaufmann, San Francisco, pp 144\u2013151"},{"issue":"200","key":"811_CR20","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675\u2013701","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"811_CR21","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1214\/aoms\/1177731944","volume":"11","author":"M Friedman","year":"1940","unstructured":"Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1):86\u201392","journal-title":"Ann Math Stat"},{"key":"811_CR22","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s00500-008-0392-y","volume":"13","author":"S Garc\u00eda","year":"2009","unstructured":"Garc\u00eda S, Fern\u00e1ndez A, Luengo J, Herrera F (2009) A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput 13:959\u2013977. doi: 10.1007\/s00500-008-0392-y","journal-title":"Soft Comput"},{"issue":"2","key":"811_CR23","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1109\/91.755399","volume":"7","author":"A Gonz\u00e1lez","year":"1999","unstructured":"Gonz\u00e1lez A, P\u00e9re R (1999) Slave: a genetic learning system based on an iterative approach. IEEE Trans Fuzzy Syst 7(2):176\u2013191. doi: 10.1109\/91.755399","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"2","key":"811_CR24","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/TSMCB.2004.842247","volume":"35","author":"SU Guan","year":"2005","unstructured":"Guan SU, Zhu F (2005) An incremental approach to genetic-algorithms-based classification. IEEE Trans Syst Man Cybern B: Cybern 35(2):227\u2013239. doi: 10.1109\/TSMCB.2004.842247","journal-title":"IEEE Trans Syst Man Cybern B: Cybern"},{"key":"811_CR25","volume-title":"Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence","author":"JH Holland","year":"1975","unstructured":"Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. University of Michigan Press, Ann Arbor"},{"key":"811_CR26","unstructured":"Holmes JH, Sager JA (2005) Rule discovery in epidemiologic surveillance data using EpiXCS: an evolutionary computation approach. In: Miksch S, Hunter J, Keravnou ET (eds) AIME. Lecture notes in computer science, vol 3581. Springer, Heidelberg, pp 444\u2013452"},{"issue":"2","key":"811_CR27","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/TSMCB.2004.842257","volume":"35","author":"H Ishibuchi","year":"2005","unstructured":"Ishibuchi H, Yamamoto T, Nakashima T (2005) Hybridization of fuzzy gbml approaches for pattern classification problems. IEEE Trans Syst Man Cybern B: Cybern 35(2):359\u2013365. doi: 10.1109\/TSMCB.2004.842257","journal-title":"IEEE Trans Syst Man Cybern B: Cybern"},{"key":"811_CR28","unstructured":"Janikow CZ (1992) Inductive learning of decision rules from attribute-based examples: a knowledge-intensive genetic algorithm approach. PhD thesis, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"key":"811_CR29","unstructured":"Kallel L, Schoenauer M (1997) Alternative random initialization in genetic algorithms. In: Proceedings of the 7th international conference on genetic algorithms. Morgan Kaufmann, pp 268\u2013275"},{"key":"811_CR30","unstructured":"Kang RG, Jung CY (2006) The improved initialization method of genetic algorithm for solving the optimization problem. In: King I, Wang J, Chan LW, Wang D (eds) Neural information processing. Lecture notes in computer science, vol 4234. Springer, Berlin-Heidelberg, pp 789\u2013796"},{"key":"811_CR31","unstructured":"Kovacs T (1999) Deletion schemes for classifier systems. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference (GECCO-99). Morgan Kaufmann, San Francisco, pp. 329\u2013336"},{"key":"811_CR32","doi-asserted-by":"crossref","unstructured":"Kovacs T (2002a) XCS\u2019s strength-based twin: part I. In: Lanzi et\u00a0al (2003), pp 61\u201380","DOI":"10.1007\/978-3-540-40029-5_5"},{"key":"811_CR33","doi-asserted-by":"crossref","unstructured":"Kovacs T (2002b) XCS\u2019s strength-based twin: part II. In: Lanzi et\u00a0al (2003), pp 81\u201398","DOI":"10.1007\/978-3-540-40029-5_6"},{"issue":"1","key":"811_CR34","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s12065-007-0003-3","volume":"1","author":"PL Lanzi","year":"2008","unstructured":"Lanzi PL (2008) Learning classifier systems: then and now. Evol Intell 1(1):63\u201382","journal-title":"Evol Intell"},{"key":"811_CR35","unstructured":"Lanzi PL, Stolzmann W, Wilson SW (eds) (2003) Learning classifier systems. 5th international workshop, IWLCS 2002, Granada, Spain, September 7\u20138, 2002, revised papers. Lecture notes in computer science, vol 2661. Springer, Berlin"},{"issue":"4","key":"811_CR36","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TEVC.2004.823466","volume":"8","author":"SJ Louis","year":"2004","unstructured":"Louis SJ, McDonnell J (2004) Learning with case-injected genetic algorithms. IEEE Trans Evol Comput 8(4):316\u2013328","journal-title":"IEEE Trans Evol Comput"},{"issue":"12","key":"811_CR37","doi-asserted-by":"crossref","first-page":"1885","DOI":"10.1016\/j.camwa.2003.07.011","volume":"47","author":"H Maaranen","year":"2004","unstructured":"Maaranen H, Miettinen K, M\u00e4kel\u00e4 MM (2004) Quasi-random initial population for genetic algorithms. Comput Math Appl 47(12):1885\u20131895","journal-title":"Comput Math Appl"},{"key":"811_CR38","unstructured":"Mitchell TM (1997) Machine learning. McGraw-Hill Higher Education"},{"key":"811_CR39","unstructured":"Nemenyi PB (1963) Distribution-free multiple comparisons. PhD thesis, Princeton University"},{"key":"811_CR40","doi-asserted-by":"crossref","unstructured":"Orriols-Puig A, Bernad\u00f3-Mansilla E (2008a) Mining imbalanced data with learning classifier systems. In: Bull L, Bernad\u00f3-Mansilla E, Holmes JH (eds) Learning classifier systems in data mining. Studies in computational intelligence, vol 125. Springer, Berlin, pp 123\u2013145. doi: 10.1007\/978-3-540-78979-6_6","DOI":"10.1007\/978-3-540-78979-6_6"},{"key":"811_CR41","doi-asserted-by":"crossref","unstructured":"Orriols-Puig A, Bernad\u00f3-Mansilla E (2008b) Revisiting UCS: description, fitness sharing, and comparison with XCS. Learning classifier systems: 10th international workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006 and 11th international workshop, IWLCS 2007, London, UK, July 8, 2007, revised selected papers, pp 96\u2013116. doi: 10.1007\/978-3-540-88138-4_6","DOI":"10.1007\/978-3-540-88138-4_6"},{"key":"811_CR42","doi-asserted-by":"crossref","unstructured":"Orriols-Puig A, Goldberg DE, Sastry K, Bernad\u00f3-Mansilla E (2007) Modeling XCS in class imbalances: population size and parameter settings. In: GECCO \u201907: proceedings of the 9th annual conference on Genetic and evolutionary computation. ACM, New York, pp 1838\u20131845. doi: 10.1145\/1276958.1277324","DOI":"10.1145\/1276958.1277324"},{"key":"811_CR43","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s12065-008-0013-9","volume":"1","author":"A Orriols-Puig","year":"2008","unstructured":"Orriols-Puig A, Casillas J, Bernad\u00f3-Mansilla E (2008) Genetic-based machine learning systems are competitive for pattern recognition. Evol Intell 1:209\u2013232. doi: 10.1007\/s12065-008-0013-9","journal-title":"Evol Intell"},{"issue":"2","key":"811_CR44","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TEVC.2008.925144","volume":"13","author":"A Orriols-Puig","year":"2009","unstructured":"Orriols-Puig A, Casillas J, Bernad\u00f3-Mansilla E (2009) Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning. IEEE Trans Evol Comput 13(2):260\u2013283","journal-title":"IEEE Trans Evol Comput"},{"key":"811_CR45","volume-title":"C4.5: programs for machine learning","author":"JR Quinlan","year":"1993","unstructured":"Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Francisco"},{"key":"811_CR46","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1613\/jair.308","volume":"5","author":"JR Quinlan","year":"1996","unstructured":"Quinlan JR (1996) Learning first-order definitions of functions. J Artif Intell Res 5:139\u2013161","journal-title":"J Artif Intell Res"},{"issue":"10","key":"811_CR47","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1016\/j.camwa.2006.07.013","volume":"53","author":"S Rahnamayan","year":"2007","unstructured":"Rahnamayan S, Tizhoosh HR, Salama MMA (2007) A novel population initialization method for accelerating evolutionary algorithms. Comput Math Appl 53(10):1605\u20131614","journal-title":"Comput Math Appl"},{"key":"811_CR48","unstructured":"Ramsey CL, Grefenstette JJ (1993) Case-based initialization of genetic algorithms. In: Proceedings of the 5th international conference on genetic algorithms. Morgan Kaufmann, San Francisco, pp 84\u201391"},{"key":"811_CR49","unstructured":"Tzima F, Mitkas P (2010) Comparing strength and accuracy-based supervised learning classifier systems. Technical report, Intelligent Systems and Software Engineering Labgroup, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece, GR-541 24"},{"issue":"5","key":"811_CR50","doi-asserted-by":"crossref","first-page":"5019","DOI":"10.1016\/j.eswa.2010.09.148","volume":"38","author":"FA Tzima","year":"2011","unstructured":"Tzima FA, Mitkas PA, Voukantsis D, Karatzas KD (2011) Sparse episode identification in environmental datasets: the case of air quality assessment. Expert Syst Appl 38(5):5019\u20135027","journal-title":"Expert Syst Appl"},{"key":"811_CR51","doi-asserted-by":"crossref","unstructured":"Vavliakis KN, Symeonidis AL, Mitkas PA (2010) Towards understanding how personality, motivation, and events trigger Web user activity. In: IEEE\/WIC\/ACM international conference on Web intelligence and intelligent agent technology","DOI":"10.1109\/WI-IAT.2010.125"},{"key":"811_CR52","doi-asserted-by":"crossref","unstructured":"Venturini G (1993) Sia: a supervised inductive algorithm with genetic search for learning attributes based concepts. In: Proceedings of the European conference on machine learning. Springer, London, pp 280\u2013296","DOI":"10.1007\/3-540-56602-3_142"},{"issue":"6","key":"811_CR53","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80\u201383","journal-title":"Biom Bull"},{"issue":"1","key":"811_CR54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/evco.1994.2.1.1","volume":"2","author":"SW Wilson","year":"1994","unstructured":"Wilson SW (1994) ZCS: A zeroth-level classifier system. Evol Comput 2(1):1\u201318","journal-title":"Evol Comput"},{"issue":"2","key":"811_CR55","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1162\/evco.1995.3.2.149","volume":"3","author":"SW Wilson","year":"1995","unstructured":"Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149\u2013175","journal-title":"Evol Comput"},{"key":"811_CR56","doi-asserted-by":"crossref","unstructured":"Wilson SW (2002) Compact rulesets from XCSI. In: IWLCS \u201901: revised papers from the 4th international workshop on advances in learning classifier systems. Springer, London, pp 197\u2013210","DOI":"10.1007\/3-540-48104-4_12"},{"key":"811_CR57","unstructured":"Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco"},{"issue":"4","key":"811_CR58","doi-asserted-by":"crossref","first-page":"3563","DOI":"10.1016\/j.eswa.2010.08.145","volume":"38","author":"G Zhang","year":"2011","unstructured":"Zhang G, Gao L, Shi Y (2011) An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Syst Appl 38(4):3563\u20133573","journal-title":"Expert Syst Appl"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-012-0811-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-012-0811-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-012-0811-y","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,23]],"date-time":"2019-06-23T14:57:47Z","timestamp":1561301867000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-012-0811-y"}},"subtitle":["Effects on model performance, readability and induction time"],"short-title":[],"issued":{"date-parts":[[2012,2,16]]},"references-count":58,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2012,7]]}},"alternative-id":["811"],"URL":"https:\/\/doi.org\/10.1007\/s00500-012-0811-y","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,2,16]]}}}