Gözetimli Siniflandiricilar ve Topluluk Temelli Sözlükler ile Biyomedikal Veri Siniflandirilmasi

Goksu Tuysuzoglu; Yusuf Yaslan

doi:10.1109/SIU.2017.7960347

Gözetimli Siniflandiricilar ve Topluluk Temelli Sözlükler ile Biyomedikal Veri Siniflandirilmasi

Goksu Tuysuzoglu^*
, Yusuf Yaslan

^*Bu çalışma için yazışmadan sorumlu yazar

Bilgisayar Mühendisliği Bölümü

Dokuz Eylul University
Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

2 Atıf (Scopus)

Özet

Nowadays, along with the development of information technologies, storage and analysis of biomedical datasets are easy in health sector. In this area, Machine Learning methods provide a great contribution for evaluation and interpretation of data. In this paper, in addition to Support Vector Machines, Decision Tree, K-Nearest Neighbors, Naive Bayes and Dictionary Learning methods, Random Feature Subspaces (RDL) and Random Instance Subspaces (BDL) methods which are the ensembles of Dictionary Learning are used in biomedical data classification. In the test results, SVM and Dictionary Learning methods, RDL and BDL, which are generated using random feature/instance subspaces achieve optimum accuracy results.

Tercüme edilen katkı başlığı	Biomedical data classification using supervised classifiers and ensemble based dictionaries
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781509064946
DOI'lar	https://doi.org/10.1109/SIU.2017.7960347
Yayın durumu	Yayınlandı - 27 Haz 2017
Etkinlik	25th Signal Processing and Communications Applications Conference, SIU 2017 - Antalya, Türkiye Süre: 15 May 2017 → 18 May 2017

Yayın serisi

Adı	2017 25th Signal Processing and Communications Applications Conference, SIU 2017

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	25th Signal Processing and Communications Applications Conference, SIU 2017
Ülke/Bölge	Türkiye
Şehir	Antalya
Periyot	15/05/17 → 18/05/17

Bibliyografik not

Publisher Copyright:
© 2017 IEEE.

Keywords

bio-medical data
classification algorithms
classifier ensemble
dictionary learning

Belgeye Erişim

10.1109/SIU.2017.7960347

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Tuysuzoglu, G., & Yaslan, Y. (2017). Gözetimli Siniflandiricilar ve Topluluk Temelli Sözlükler ile Biyomedikal Veri Siniflandirilmasi. In 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 Makale 7960347 (2017 25th Signal Processing and Communications Applications Conference, SIU 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2017.7960347

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title = "G{\"o}zetimli Siniflandiricilar ve Topluluk Temelli S{\"o}zl{\"u}kler ile Biyomedikal Veri Siniflandirilmasi",

abstract = "Nowadays, along with the development of information technologies, storage and analysis of biomedical datasets are easy in health sector. In this area, Machine Learning methods provide a great contribution for evaluation and interpretation of data. In this paper, in addition to Support Vector Machines, Decision Tree, K-Nearest Neighbors, Naive Bayes and Dictionary Learning methods, Random Feature Subspaces (RDL) and Random Instance Subspaces (BDL) methods which are the ensembles of Dictionary Learning are used in biomedical data classification. In the test results, SVM and Dictionary Learning methods, RDL and BDL, which are generated using random feature/instance subspaces achieve optimum accuracy results.",

keywords = "bio-medical data, classification algorithms, classifier ensemble, dictionary learning",

author = "Goksu Tuysuzoglu and Yusuf Yaslan",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 25th Signal Processing and Communications Applications Conference, SIU 2017 ; Conference date: 15-05-2017 Through 18-05-2017",

year = "2017",

month = jun,

day = "27",

doi = "10.1109/SIU.2017.7960347",

language = "T{\"u}rk{\c c}e",

series = "2017 25th Signal Processing and Communications Applications Conference, SIU 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2017 25th Signal Processing and Communications Applications Conference, SIU 2017",

address = "United States",

}

Tuysuzoglu, G & Yaslan, Y 2017, Gözetimli Siniflandiricilar ve Topluluk Temelli Sözlükler ile Biyomedikal Veri Siniflandirilmasi. in 2017 25th Signal Processing and Communications Applications Conference, SIU 2017., 7960347, 2017 25th Signal Processing and Communications Applications Conference, SIU 2017, Institute of Electrical and Electronics Engineers Inc., 25th Signal Processing and Communications Applications Conference, SIU 2017, Antalya, Türkiye, 15/05/17. https://doi.org/10.1109/SIU.2017.7960347

Gözetimli Siniflandiricilar ve Topluluk Temelli Sözlükler ile Biyomedikal Veri Siniflandirilmasi. / Tuysuzoglu, Goksu; Yaslan, Yusuf.
2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7960347 (2017 25th Signal Processing and Communications Applications Conference, SIU 2017).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Gözetimli Siniflandiricilar ve Topluluk Temelli Sözlükler ile Biyomedikal Veri Siniflandirilmasi

AU - Tuysuzoglu, Goksu

AU - Yaslan, Yusuf

PY - 2017/6/27

Y1 - 2017/6/27

N2 - Nowadays, along with the development of information technologies, storage and analysis of biomedical datasets are easy in health sector. In this area, Machine Learning methods provide a great contribution for evaluation and interpretation of data. In this paper, in addition to Support Vector Machines, Decision Tree, K-Nearest Neighbors, Naive Bayes and Dictionary Learning methods, Random Feature Subspaces (RDL) and Random Instance Subspaces (BDL) methods which are the ensembles of Dictionary Learning are used in biomedical data classification. In the test results, SVM and Dictionary Learning methods, RDL and BDL, which are generated using random feature/instance subspaces achieve optimum accuracy results.

AB - Nowadays, along with the development of information technologies, storage and analysis of biomedical datasets are easy in health sector. In this area, Machine Learning methods provide a great contribution for evaluation and interpretation of data. In this paper, in addition to Support Vector Machines, Decision Tree, K-Nearest Neighbors, Naive Bayes and Dictionary Learning methods, Random Feature Subspaces (RDL) and Random Instance Subspaces (BDL) methods which are the ensembles of Dictionary Learning are used in biomedical data classification. In the test results, SVM and Dictionary Learning methods, RDL and BDL, which are generated using random feature/instance subspaces achieve optimum accuracy results.

KW - bio-medical data

KW - classification algorithms

KW - classifier ensemble

KW - dictionary learning

UR - https://www.scopus.com/pages/publications/85026290558

U2 - 10.1109/SIU.2017.7960347

DO - 10.1109/SIU.2017.7960347

M3 - Konferans katkısı

AN - SCOPUS:85026290558

T3 - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

BT - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 25th Signal Processing and Communications Applications Conference, SIU 2017

Y2 - 15 May 2017 through 18 May 2017

ER -

Tuysuzoglu G, Yaslan Y. Gözetimli Siniflandiricilar ve Topluluk Temelli Sözlükler ile Biyomedikal Veri Siniflandirilmasi. In 2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 7960347. (2017 25th Signal Processing and Communications Applications Conference, SIU 2017). doi: 10.1109/SIU.2017.7960347

Gözetimli Siniflandiricilar ve Topluluk Temelli Sözlükler ile Biyomedikal Veri Siniflandirilmasi

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap