مشخصات کلی A comparison of performance of K-complex classification methods using feature selection
نویسنده کتاب (Author):
انتشارات (Publisher):
ویرایش و نوع فایل (Edition/Format):
Downloadable article : English
منبع (Database):
عنوان ژورنال (Publication):
elena-hernandez-pereira-veronica-bolon-canedo-noelia-sanchez-maron%cc%83o-diego-alvarez-estevez-vicente-moret-bonillo-amparo-alonso-betanzos-information-sciences-i
موضوع (Subject):
Feature selection Machine learning K-complex classification.
توضیحات خلاصه (Summary):
[Abstract] The main objective of this work is to obtain a method that achieves the best accuracy results with a low false positive rate in the classification of K-complexes, a kind of transient waveform found in the Electroencephalogram. With this in mind, the capabilities of several machine learning techniques were tried. The inputs for the models were a set of features based on amplitude and duration measurements obtained from waveforms to be classified. Among all the classifiers tested, the Support Vector Machine obtained the best results with an accuracy of 88.69%. Finally, to enhance the generalization capabilities of the classifiers, while at the same time discarding the existing irrelevant features, feature selection methods were employed. After this process, the classification performance was significantly improved. The best result was obtained applying a correlation-based filter, achieving a 91.40% of accuracy using only 36% of the total input features. Read more…
ژانر / فرم:info:eu-repo/semantics/article
موضوع:Internet resource
نوع منبع:Internet Resource, Article
تمام نویسندگان / همکاران: Hernández Pereira, Elena; Bolón Canedo, Verónica; Sánchez Maroño, Noelia; Álvarez Estévez, Diego; Moret Bonillo, Vicente; Alonso Betanzos, Amparo
شناسه OCLC:979265368
Language Note:English
فهرست محتوا:0020-0255 1872-6291 http://hdl.handle.net/2183/18099 10.1016/j.ins.2015.08.022
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