{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:44:43Z","timestamp":1760708683650},"reference-count":8,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Parallel Process. Lett."],"published-print":{"date-parts":[[2014,3]]},"abstract":"<jats:p> Current associative classification (AC) algorithms generate only the most obvious class linked with a rule in the training data set and ignore all other classes. We handle this problem by proposing a learning algorithm based on AC called Multi-label Classifiers based Associative Classification (MCAC) that learns rules associated with multiple classes from single label data. MCAC algorithm extracts classifiers from the whole training data set discovering all possible classes connected with a rule as long as they have sufficient training data representation. Another distinguishing feature of the MCAC algorithm is the classifier building method that cuts down the number of rules treating one known problem in AC mining which is the exponential growth of rules. Experimentations using real application data related to a complex scheduling problem known as the trainer timetabling problem reveal that MCAC's predictive accuracy is highly competitive if contrasted with known AC algorithms. <\/jats:p>","DOI":"10.1142\/s0129626414500017","type":"journal-article","created":{"date-parts":[[2014,3,28]],"date-time":"2014-03-28T20:36:06Z","timestamp":1396038966000},"page":"1450001","source":"Crossref","is-referenced-by-count":15,"title":["Multi-Label Rules Algorithm Based Associative Classification"],"prefix":"10.1142","volume":"24","author":[{"given":"Neda","family":"Abdelhamid","sequence":"first","affiliation":[{"name":"School of Computer Science and Informatics, De Montfort University, Leicester, LE1 9BH, UK"}]},{"given":"Aladdin","family":"Ayesh","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, De Montfort University, Leicester, LE1 9BH, UK"}]},{"given":"Wael","family":"Hadi","sequence":"additional","affiliation":[{"name":"MIS Dept., Petra University, Amman, Jordan"}]}],"member":"219","published-online":{"date-parts":[[2014,3,28]]},"reference":[{"key":"p_4","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2010.04.007"},{"key":"p_6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-32063-7_4"},{"key":"p_9","first-page":"459","author":"Jiang Z.","year":"2013","journal-title":"Springer Berlin Heidelberg"},{"key":"p_14","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007649029923"},{"key":"p_16","doi-asserted-by":"publisher","DOI":"10.1142\/S0219649210002486"},{"key":"p_17","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2006.10.008"},{"key":"p_18","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888907001026"},{"key":"p_27","first-page":"791","volume":"8","author":"Zhu Y.","year":"2012","journal-title":"Journal of Computational Information Systems"}],"container-title":["Parallel Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0129626414500017","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T02:40:11Z","timestamp":1565145611000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0129626414500017"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,3]]},"references-count":8,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2014,3,28]]},"published-print":{"date-parts":[[2014,3]]}},"alternative-id":["10.1142\/S0129626414500017"],"URL":"https:\/\/doi.org\/10.1142\/s0129626414500017","relation":{},"ISSN":["0129-6264","1793-642X"],"issn-type":[{"value":"0129-6264","type":"print"},{"value":"1793-642X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,3]]}}}