{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T13:18:23Z","timestamp":1769692703813,"version":"3.49.0"},"reference-count":32,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,11,22]],"date-time":"2020-11-22T00:00:00Z","timestamp":1606003200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011665","name":"Deanship of Scientific Research, King Saud University","doi-asserted-by":"publisher","award":["RG-1439-035"],"award-info":[{"award-number":["RG-1439-035"]}],"id":[{"id":"10.13039\/501100011665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2020,11,22]]},"abstract":"<jats:p>Text classification has many applications in text processing and information retrieval. Instance-based learning (IBL) is among the top-performing text classification methods. However, its effectiveness depends on the distance function it uses to determine similar documents. In this study, we evaluate some popular distance measures\u2019 performance and propose new ones that exploit word frequencies and the ordinal relationship between them. In particular, we propose new distance measures that are based on the value distance metric (VDM) and the inverted specific-class distance measure (ISCDM). The proposed measures are suitable for documents represented as vectors of word frequencies. We compare these measures\u2019 performance with their original counterparts and with powerful Na\u00efve Bayesian-based text classification algorithms. We evaluate the proposed distance measures using the kNN algorithm on 18 benchmark text classification datasets. Our empirical results reveal that the distance metrics for nominal values render better classification results for text classification than the Euclidean distance measure for numeric values. Furthermore, our results indicate that ISCDM substantially outperforms VDM, but it is also more susceptible to make use of the ordinal nature of term-frequencies than VDM. Thus, we were able to propose more ISCDM-based distance measures for text classification than VDM-based measures. We also compare the proposed distance measures with Na\u00efve Bayesian-based text classification, namely, multinomial Na\u00efve Bayes (MNB), complement Na\u00efve Bayes (CNB), and the one-versus-all-but-one (OVA) model. It turned out that when kNN uses some of the proposed measures, it outperforms NB-based text classifiers for most datasets.<\/jats:p>","DOI":"10.1155\/2020\/4717984","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:06:03Z","timestamp":1606176363000},"page":"1-10","source":"Crossref","is-referenced-by-count":5,"title":["Improved Distance Functions for Instance-Based Text Classification"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2457-9961","authenticated-orcid":true,"given":"Khalil","family":"El Hindi","sequence":"first","affiliation":[{"name":"Department of Computer Science, King Saud University, Riyadh, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bayan","family":"Abu Shawar","sequence":"additional","affiliation":[{"name":"Cypersecurity Department, School of Engineering, Al Ain University, Abu Dhabi, UAE"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reem","family":"Aljulaidan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, King Saud University, Riyadh, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hussien","family":"Alsalamn","sequence":"additional","affiliation":[{"name":"Department of Computer Science, King Saud University, Riyadh, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1145\/345508.345569"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1145\/505282.505283"},{"key":"3","first-page":"158","article-title":"Message classification in the call center","author":"S. 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