{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T23:26:59Z","timestamp":1770593219080,"version":"3.49.0"},"reference-count":34,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2017,7,7]],"date-time":"2017-07-07T00:00:00Z","timestamp":1499385600000},"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":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2017,8,24]]},"abstract":"<jats:p>\n                    In this paper, we propose\n                    <jats:italic>NPC<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>c<\/jats:italic>\n                    <\/jats:sub>\n                    , a new Na\u00efve Possibilistic Classifier for categorical data. The proposed classifier relies on the Bayesian structure of the Na\u00efve Bayes Classifier for categorical data (\n                    <jats:italic>NBC<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>c<\/jats:italic>\n                    <\/jats:sub>\n                    ) which stands for an interesting pattern when dealing with discrete attributes. However, unlike\n                    <jats:italic>NBC<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>c<\/jats:italic>\n                    <\/jats:sub>\n                    , the proposed\n                    <jats:italic>NPC<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>c<\/jats:italic>\n                    <\/jats:sub>\n                    is based on the possibilistic formalism as an efficient fuzzy-sets-based alternative to the probabilistic one when handling uncertain data. Distinctively, we use the possibilistic approach to estimate beliefs from categorical data and a Generalized Minimum-based classification algorithm (G-Min) as a novel algorithm to make decision from possibilistic beliefs. Experimental evaluations on 12 datasets taken from University of California Irvine (UCI) and containing all categorical data, confirm the effectiveness of the proposed new G-Min-based\n                    <jats:italic>NPC<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>c<\/jats:italic>\n                    <\/jats:sub>\n                    . With the used datasets, the proposed classifier outperforms the commonly-used classifiers for categorical data including\n                    <jats:italic>NBC<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>c<\/jats:italic>\n                    <\/jats:sub>\n                    , C4.5-based decision tree and RIPPER-based classifier. Moreover, it outperforms the two versions of\n                    <jats:italic>NPC<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>c<\/jats:italic>\n                    <\/jats:sub>\n                    using commonly-used possibilistic classification algorithms which are based on respectively, the product and the minimum operators. Consequently, we prove the efficiency of the possibilistic approach together with the G-Min algorithm for the classification of categorical data.\n                  <\/jats:p>","DOI":"10.3233\/jifs-15372","type":"journal-article","created":{"date-parts":[[2017,7,7]],"date-time":"2017-07-07T11:47:34Z","timestamp":1499428054000},"page":"1723-1731","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["A new classifier for categorical data based on a possibilistic estimation and a novel generalized minimum-based algorithm"],"prefix":"10.1177","volume":"33","author":[{"given":"Karim","family":"Baati","sequence":"first","affiliation":[{"name":"REGIM-Lab.: REsearch Groups on Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), Sfax, Tunisia"},{"name":"Esprit School of Engineering, Tunis, Tunisia"}]},{"given":"Tarek M.","family":"Hamdani","sequence":"additional","affiliation":[{"name":"REGIM-Lab.: REsearch Groups on Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), Sfax, Tunisia"},{"name":"Taibah University, College Of Science and arts at Al-Ula, Al-Madinah al-Munawwarah, KSA"}]},{"given":"Adel M.","family":"Alimi","sequence":"additional","affiliation":[{"name":"REGIM-Lab.: REsearch Groups on Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), Sfax, Tunisia"}]},{"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[{"name":"Machines Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, WA, USA"}]}],"member":"179","published-online":{"date-parts":[[2017,7,7]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"AgrestiA. 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