{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T10:47:13Z","timestamp":1768646833917,"version":"3.49.0"},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The purpose of this study was to generate more concise rule extraction from the Recursive-Rule Extraction (Re-RX) algorithm by replacing the C4.5 program currently employed in Re-RX with the J48graft algorithm. Experiments were subsequently conducted to determine rules for six different two-class mixed datasets having discrete and continuous attributes and to compare the resulting accuracy, comprehensibility and conciseness. When working with the CARD1, CARD2, CARD3, German, Bene1 and Bene2 datasets, Re-RX with J48graft provided more concise rules than the original Re-RX algorithm. The use of Re-RX with J48graft resulted in 43.2%, 37% and 21% reductions in rules in the case of the German, Bene1 and Bene2 datasets compared to Re-RX. Furthermore, the Re-RX with J48graft showed 8.87% better accuracy than the Re-RX algorithm for the German dataset. These results confirm that the application of Re-RX in conjunction with J48graft has the capacity to facilitate migration from existing data systems toward new concise analytic systems and Big Data.<\/jats:p>","DOI":"10.1515\/jaiscr-2016-0004","type":"journal-article","created":{"date-parts":[[2016,1,15]],"date-time":"2016-01-15T16:14:04Z","timestamp":1452874444000},"page":"35-44","source":"Crossref","is-referenced-by-count":25,"title":["Recursive-Rule Extraction Algorithm With J48graft And Applications To Generating Credit Scores"],"prefix":"10.1515","volume":"6","author":[{"given":"Yoichi","family":"Hayashi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Meiji University Kawasaki 214-8571, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuki","family":"Tanaka","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Meiji University Kawasaki 214-8571, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomohiro","family":"Takagi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Meiji University Kawasaki 214-8571, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takamichi","family":"Saito","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Meiji University Kawasaki 214-8571, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hideaki","family":"Iiduka","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Meiji University Kawasaki 214-8571, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroaki","family":"Kikuchi","sequence":"additional","affiliation":[{"name":"Department of Frontier Media Science,, Meiji University Nakano-ku, Tokyo 164-8525, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guido","family":"Bologna","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of Applied Sciences of Western Switzerland Rue de la prairie 4, 1204 Geneva, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sushmita","family":"Mitra","sequence":"additional","affiliation":[{"name":"Sushmita Mitra Machine Intelligence Unit, Indian Statistical Institute 203 B.T. Road, Kolkata 700 108, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2016,1,13]]},"container-title":["Journal of Artificial Intelligence and Soft Computing Research"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/content.sciendo.com\/view\/journals\/jaiscr\/6\/1\/article-p35.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.sciendo.com\/article\/10.1515\/jaiscr-2016-0004","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T21:04:20Z","timestamp":1618002260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciendo.com\/article\/10.1515\/jaiscr-2016-0004"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,1,1]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,1,13]]},"published-print":{"date-parts":[[2016,1,1]]}},"alternative-id":["10.1515\/jaiscr-2016-0004"],"URL":"https:\/\/doi.org\/10.1515\/jaiscr-2016-0004","relation":{},"ISSN":["2083-2567"],"issn-type":[{"value":"2083-2567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,1,1]]}}}