A new dynamic classifier selection method for text classification

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dc.contributor.authorTerzi, Ismail
dc.contributor.authorUysal, Alper Kursat
dc.date.accessioned2026-01-24T12:26:46Z
dc.date.available2026-01-24T12:26:46Z
dc.date.issued2024
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractThe primary objective of employing multiple classifier systems (MCS) in pattern recognition is to enhance classification accuracy. Dynamic classifier selection (DCS) and dynamic ensemble selection (DES) are two purposeful forms of multiple classifier systems. While DES involves the selection of a classifier set followed by decision combination, DCS opts for the choice of a single competent classifier, eliminating the necessity for classifier combination. As a consequence, DCS methods exhibit superior efficiency in terms of processing time and memory usage compared to DES methods. Moreover, a substantial performance gap exists between the performance of Oracle and both DES and DCS methods. In this study, we introduce a novel method termed dynamic classifier selection technique-decision quotient (DCS-DQ) for text classification based on dynamic classifier selection. Our experimental investigation encompasses four distinct text datasets, with classification accuracy and macro F1-score serving as the primary evaluation criteria. The proposed DCS-DQ method is subjected to comparison with seven state-of-the-art DCS methods. Based on our empirical findings, the DCS-DQ method outperforms the other seven DCS methods in terms of classification accuracy across the majority of feature sizes. Notably, in the Reuters dataset, the classification accuracy of DCS-DQ surpasses that of other DCS methods for all feature sizes except when the feature size is 100. Similarly, in the Ohsumed dataset, the DCS-DQ method demonstrates significant performance improvement, with an accuracy value of 77.02% for 3000 features compared to the maximum accuracy value of 72.74% achieved by the DCS method MCB. Additionally, the performance of the proposed DCS-DQ method closely aligns with the oracle performance compared to the other methods. In conclusion, our proposed DCS-DQ method holds promise for significantly improving classification accuracy in text classification literature.
dc.identifier.doi10.55730/1300-0632.4092
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85200220490
dc.identifier.scopusqualityQ2
dc.identifier.trdizinid1252404
dc.identifier.urihttps://doi.org/10.55730/1300-0632.4092
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1252404
dc.identifier.urihttps://hdl.handle.net/20.500.12868/4920
dc.identifier.volume32
dc.identifier.wosWOS:001280878700009
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectText classification
dc.subjectdynamic classifier selection
dc.subjectmultiple classifier systems
dc.subjectDCS-DQ
dc.titleA new dynamic classifier selection method for text classification
dc.typeArticle

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