{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T04:14:01Z","timestamp":1741666441119,"version":"3.38.0"},"reference-count":0,"publisher":"SAGE Publications","issue":"6_suppl","license":[{"start":{"date-parts":[[2014,11,1]],"date-time":"2014-11-01T00:00:00Z","timestamp":1414800000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Data Analysis: An International Journal"],"published-print":{"date-parts":[[2014,11]]},"abstract":"<jats:p> In Associative Classification, building a classifier based on Class Association Rules (CARs) consists in finding an ordered CAR list by applying a rule ordering strategy, and selecting a satisfaction mechanism to determine the class of unseen transactions. In this paper, we introduce four novel hybrid rule ordering strategies; the first three combine the Netconf measure with different Support-Confidence based rule ordering strategies. The fourth strategy combines the Netconf measure with a rule ordering strategy based on the CAR's size. Additionally, we combine the proposed strategies with a novel \u201cDynamic K\u201d satisfaction mechanism. Experiments over several datasets show that the proposed rule ordering strategies jointly with the \u201cDynamic K\u201d satisfaction mechanism allow improving the performance of CAR-based classifiers. <\/jats:p>","DOI":"10.3233\/ida-140711","type":"journal-article","created":{"date-parts":[[2018,10,18]],"date-time":"2018-10-18T17:03:41Z","timestamp":1539882221000},"page":"S89-S100","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Combining hybrid rule ordering strategies based on netconf and a novel satisfaction mechanism for CAR-based classifiers"],"prefix":"10.1177","volume":"18","author":[{"given":"R.","family":"Hern\u00e1ndez-Le\u00f3n","sequence":"first","affiliation":[{"name":"Centro de Aplicaciones de Tecnolog\u00edas de Avanzada, Havana, Cuba"}]},{"given":"Jes\u00fas A.","family":"Carrasco-Ochoa","sequence":"additional","affiliation":[{"name":"Instituto Nacional de Astrof\u00edsica \u00d3ptica y Electr\u00f3nica, Puebla, M\u00e9xico"}]},{"given":"Jos\u00e9 Fco.","family":"Mart\u00ednez-Trinidad","sequence":"additional","affiliation":[{"name":"Instituto Nacional de Astrof\u00edsica \u00d3ptica y Electr\u00f3nica, Puebla, M\u00e9xico"}]},{"given":"J.","family":"Hern\u00e1ndez-Palancar","sequence":"additional","affiliation":[{"name":"Centro de Aplicaciones de Tecnolog\u00edas de Avanzada, Havana, Cuba"}]}],"member":"179","published-online":{"date-parts":[[2014,11,1]]},"container-title":["Intelligent Data Analysis: An International Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IDA-140711","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IDA-140711","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T13:22:21Z","timestamp":1741612941000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IDA-140711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,11]]},"references-count":0,"journal-issue":{"issue":"6_suppl","published-print":{"date-parts":[[2014,11]]}},"alternative-id":["10.3233\/IDA-140711"],"URL":"https:\/\/doi.org\/10.3233\/ida-140711","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"type":"print","value":"1088-467X"},{"type":"electronic","value":"1571-4128"}],"subject":[],"published":{"date-parts":[[2014,11]]}}}