Basit öğe kaydını göster

dc.contributor.authorAltınel, Berna
dc.contributor.authorDiri, Banu
dc.contributor.authorGaniz, Murat Can
dc.date.accessioned2015-09-15T09:32:40Z
dc.date.available2015-09-15T09:32:40Z
dc.date.issued2014-12-24
dc.identifier.citationAltınel, B., Diri, B., Ganiz, M.C. (2014). A novel semantic smoothing kernel for text classification with class-based weighting. Knowledge-Based Systems, 1-13. http://dx.doi.org/10.1016/j.knosys.2015.07.008en_US
dc.identifier.issn0950-7051
dc.identifier.urihttp://dx.doi.org/10.1016/j.knosys.2015.07.008
dc.identifier.urihttp://hdl.handle.net/11376/2091
dc.descriptionAltınel, Berna (Dogus Author), Diri, Banu (Dogus Author), Ganiz, Murat Can (Dogus Author) -- #articleinpress#en_US
dc.descriptionAltınel, Berna (Dogus Author), Diri, Banu (Dogus Author), Ganiz, Murat Can (Dogus Author)en_US
dc.description.abstractIn this study, we propose a novel methodology to build a semantic smoothing kernel to use with Support Vector Machines (SVM) for text classification. The suggested approach is based on two key concepts; class-based term weighting and changing the orthogonality of vector space. A class-based term weighting methodology is used for transformation of documents from the original space to the feature space. This class-based weighting basically groups terms based on their importance for each class and consequently smooths the representation of documents. This is accomplished by changing the orthogonality of the Vector Space Model (VSM) with introducing class-based dependencies between terms. As a result, on the extreme case, two documents can be seen as similar even if they do not share any terms but their terms are similarly weighted for a particular class. The resulting semantic kernel can directly make use of class information in extracting semantic information between terms, therefore it can be considered as a supervised kernel. For our experimental evaluation, we analyze the performance of the suggested kernel with a large number of experiments on benchmark textual datasets and present results with respect to varying experimental conditions. To the best of our knowledge, this is the first study to use class-based term weighting in order to build a supervised semantic kernel for SVM. We compare our results with kernels that are commonly used in SVM such as linear kernel, polynomial kernel, Radial Basis Function (RBF) kernel and with several corpus-based semantic kernels. According to our experimental results the proposed method favorably improves classification accuracy over linear kernel and several corpus-based semantic kernels in terms of both accuracy and speed.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/j.knosys.2015.07.008en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClass - Based Term Weightingen_US
dc.subjectSemantic Kernelen_US
dc.subjectSemantic Smoothing Kernelen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectText Classificationen_US
dc.titleA novel semantic smoothing kernel for text classification with class-based weightingen_US
dc.typearticleen_US
dc.relation.journalKnowledge-Based Systemsen_US
dc.contributor.departmentDoğuş Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.authorIDTR193315en_US
dc.contributor.authorIDTR25308en_US
dc.contributor.authorIDTR23878en_US
dc.identifier.startpage1en_US
dc.identifier.endpage13en_US


Bu öğenin dosyaları:

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster