{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T15:27:01Z","timestamp":1696001221615},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2007,10,11]],"date-time":"2007-10-11T00:00:00Z","timestamp":1192060800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nat Comput"],"published-print":{"date-parts":[[2009,3]]},"DOI":"10.1007\/s11047-007-9056-6","type":"journal-article","created":{"date-parts":[[2007,10,10]],"date-time":"2007-10-10T14:17:04Z","timestamp":1192025824000},"page":"101-120","source":"Crossref","is-referenced-by-count":15,"title":["Observer-invariant histopathology using genetics-based machine learning"],"prefix":"10.1007","volume":"8","author":[{"given":"Xavier","family":"Llor\u00e0","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anusha","family":"Priya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohit","family":"Bhargava","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2007,10,11]]},"reference":[{"key":"9056_CR1","doi-asserted-by":"crossref","unstructured":"Amdahl G (1967) Validity of the single processor approach to achieving large-scale computing capabilities. In Proceedings of the American federation of information processing societies conference (AFIPS). 30:483\u2013485 AFIPS","DOI":"10.1145\/1465482.1465560"},{"key":"9056_CR2","unstructured":"Bacardit J, Butz M (2006) Advances at the frontier of Learning Classifier Systems. Chapter data mining in Learning Classifier Systems: Comparing XCS with GAssist, vol I. Springer"},{"key":"9056_CR3","unstructured":"Bacardit J, Krasnogor N (2006) Biohel: Bioinformatics-oriented hierarchical evolutionary learning (Nottingham ePrints). University of Nottingham"},{"key":"9056_CR4","unstructured":"Barry A, Drugowitsch J (1997) LCSWeb: the LCS wiki. http:\/\/www.lcsweb.cs.bath.ac.uk\/"},{"key":"9056_CR5","unstructured":"Bernad\u00f3 E, Llor\u00e0 X, Garrell J (2001) Advances in Learning Classifier Systems: 4th international workshop (IWLCS 2001). Chapter XCS and GALE: a comparative study of two Learning Classifier Systems with six other learning algorithms on classification tasks. Springer Berlin, Heidelberg, pp 115\u2013132"},{"issue":"7","key":"9056_CR6","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1016\/j.bbamem.2006.05.007","volume":"1758","author":"R Bhargava","year":"2006","unstructured":"Bhargava R, Fernandez D, Hewitt S, Levin I (2006) High throughput assessment of cells and tissues: Bayesian classification of spectral metrics from infrared vibrational spectroscopic imaging data. Biochemica et Biophisica Acta 1758(7):830\u2013845","journal-title":"Biochemica et Biophisica Acta"},{"key":"9056_CR7","doi-asserted-by":"crossref","unstructured":"Cant\u00fa-Paz E (2000) Efficient and accurate parallel genetic algorithms. Kluwer Academic Publishers","DOI":"10.1007\/978-1-4615-4369-5"},{"key":"9056_CR8","doi-asserted-by":"crossref","unstructured":"Cord\u00f3n O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems. Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific","DOI":"10.1142\/4177"},{"issue":"4","key":"9056_CR9","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1038\/nbt1080","volume":"23","author":"D Fernandez","year":"2005","unstructured":"Fernandez D, Bhargava R, Hewitt S, Levin I (2005) Infrared spectroscopic imaging for histopathologic recognition. Nat Biotechnol 23(4):469\u2013474","journal-title":"Nat Biotechnol"},{"key":"9056_CR10","unstructured":"Flockhart I (1995) GA-MINER: parallel data mining with hierarchical genetic algorithms (final report). (Technical Report Technical Report EPCCAIKMS-GA-MINER-REPORT 1.0). University of Edinburgh"},{"key":"9056_CR11","doi-asserted-by":"crossref","unstructured":"Gabriel E, Fagg G, Bosilca G, Angskun T, Dongarra J, Squyres J, Sahay V, Kambadur P, Barrett B, Lumsdaine A, Castain R, Daniel D, Graham R, Woodall T (2004) Open MPI: goals, concept, and design of a next generation MPI implementation. In Proceedings of the 11th European PVMMPI Users\u2019 group meeting Springer","DOI":"10.1007\/978-3-540-30218-6_19"},{"key":"9056_CR12","unstructured":"Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Professional"},{"key":"9056_CR13","doi-asserted-by":"crossref","unstructured":"Goldberg D (2002) The design of innovation: lessons from and for competent genetic algorithms. Springer","DOI":"10.1007\/978-1-4757-3643-4"},{"key":"9056_CR14","unstructured":"Grama A, Gupta A, Karypis G, Kumar V (2003) Introduction to parallel computing. Addison-Wesley"},{"key":"9056_CR15","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1023\/A:1022631118932","volume":"11","author":"R Holte","year":"1993","unstructured":"Holte R (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn 11:63\u201391","journal-title":"Mach Learn"},{"key":"9056_CR16","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1046\/j.1464-410X.2002.02990.x","volume":"90","author":"J-B Lattouf","year":"2002","unstructured":"Lattouf J-B, Saad F (2002) Gleason score on biopsy: is it reliable for predcting the final grade on pathology? BJU Int 90:694\u2013699","journal-title":"BJU Int"},{"key":"9056_CR17","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1146\/annurev.physchem.56.092503.141205","volume":"56","author":"I Levin","year":"2005","unstructured":"Levin I, Bhargava R (2005) Fourier transform infrared vibrational spectroscopic imaging: integrating microscopy and molecular recognition. Annu Rev Phys Chem 56: 429\u2013474","journal-title":"Annu Rev Phys Chem"},{"key":"9056_CR18","unstructured":"Llor\u00e0 X (2002) Genetics-based machine learning using fine-grained parallelism for data mining. Doctoral dissertation, Enginyeria i Arquitectura La Salle. Ramon Llull University, Barcelona, Catalonia, European Union"},{"key":"9056_CR19","unstructured":"Llor\u00e0 X (2006) Learning Classifier Systems and other genetics-based machine learning Blog. http:\/\/www-illigal.ge.uiuc.edulcs-n-gbml\/"},{"key":"9056_CR20","unstructured":"Llor\u00e0 X, Garrell J (2001) Knowledge-independent data mining with fine-grained parallel evolutionary algorithms. In Proceedings of the genetic and evolutionary computation conference (GECCO\u20192001). Morgan Kaufmann Publishers, pp 461\u2013468"},{"issue":"3","key":"9056_CR21","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1162\/106365603322365306","volume":"11","author":"X Llor\u00e0","year":"2003","unstructured":"Llor\u00e0 X, Goldberg D (2003) Bounding the effect of noise in multiobjective Learning Classifier Systems. Evol Comput J 11(3):279\u2013298","journal-title":"Evol Comput J"},{"key":"9056_CR22","doi-asserted-by":"crossref","unstructured":"Llor\u00e0 X, Sastry K (2006) Fast rule matching for Learning Classifier Systems via vector instructions. In Proceedings of the 2006 genetic and evolutionary computation conference. ACM Press, pp 1513\u20131520","DOI":"10.1145\/1143997.1144244"},{"key":"9056_CR23","doi-asserted-by":"crossref","unstructured":"Llor\u00e0 X, Sastry K, Goldberg D (2005) The compact classifier system: motivation, analysis and first results. In Proceedings of the congress on evolutionary computation, vol 1. IEEE press, (Also as IlliGAL TR No 2005019, pp 596\u2013603)","DOI":"10.1145\/1068009.1068328"},{"key":"9056_CR24","unstructured":"Llor\u00e0 X, Sastry K, Goldberg D, de la Ossa L (2007) The \u03c7-ary extended compact classifier system: linkage learning in Pittsburgh LCS. In Advances at the frontier of Learning Classifier Systems, vol II. IlliGAL report no 2006015. Springer, pp (in preparation)"},{"key":"9056_CR25","unstructured":"Merz CJ, Murphy PM (1998) UCI repository for machine learning data-bases. http:\/\/www.ics.uci.edu\/~mlearn\/MLRepository.html"},{"key":"9056_CR26","unstructured":"Mitchell T (1997) Machine learning. McGraw Hill"},{"key":"9056_CR27","unstructured":"Orriols-Puig A, Bernad\u00f3-Mansilla E (2006) A further look at UCS classifier system. In Proceedings of the 8th annual conference on genetic and evolutionary computation workshop program. ACM Press"},{"key":"9056_CR28","unstructured":"Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann"},{"issue":"3","key":"9056_CR29","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1162\/106365603322365315","volume":"11","author":"C Stone","year":"2003","unstructured":"Stone C, Bull L (2003) For real! XCS with continuous-valued inputs. Evol Comput J 11(3):279\u2013298","journal-title":"Evol Comput J"},{"issue":"2","key":"9056_CR30","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1162\/evco.1995.3.2.149","volume":"3","author":"S Wilson","year":"1995","unstructured":"Wilson S (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149\u2013175","journal-title":"Evol Comput"},{"key":"9056_CR31","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/3-540-45027-0_11","volume":"1813","author":"S Wilson","year":"2000","unstructured":"Wilson S (2000a) Get real! XCS with continuous-valued inputs. Lect Notes Comput Sci 1813:209\u2013219","journal-title":"Lect Notes Comput Sci"},{"key":"9056_CR32","doi-asserted-by":"crossref","unstructured":"Wilson S (2000b) Mining oblique data with xcs. In Revised papers of the 3th international workshop on Learning Classifier Systems (IWLCS 2000). Springer, pp 158\u2013176","DOI":"10.1007\/3-540-44640-0_11"}],"container-title":["Natural Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11047-007-9056-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11047-007-9056-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11047-007-9056-6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T03:46:03Z","timestamp":1559360763000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11047-007-9056-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2007,10,11]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2009,3]]}},"alternative-id":["9056"],"URL":"https:\/\/doi.org\/10.1007\/s11047-007-9056-6","relation":{},"ISSN":["1567-7818","1572-9796"],"issn-type":[{"value":"1567-7818","type":"print"},{"value":"1572-9796","type":"electronic"}],"subject":[],"published":{"date-parts":[[2007,10,11]]}}}