Artificial immune recognition system based classifier ensemble on the different feature subsets for detecting the cardiac disorders from SPECT images
Küçük Resim Yok
Tarih
2007
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER-VERLAG BERLIN
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Combining outputs of multiple classifiers is one of most important techniques for improving classification accuracy. In this paper, we present a new classifier ensemble based on artificial immune recognition system (AIRS) classifier and independent component analysis (ICA) for detecting the cardiac disorders from SPECT images. Firstly, the dimension of SPECT (Single Photon Emission Computed Tomography) images dataset, which has 22 binary features, was reduced to 3, 4, and 5 features using FastICA algorithm. Three different feature subsets were obtained in this way. Secondly, the obtained feature subsets were classified by AIRS classifier and then stored the outputs obtained from AIRS classifier into the result matrix. The exact result that denote whether subject has cardiac disorder or not was obtained by averaging the outputs obtained from AIRS classifier into the result matrix. While only AIRS classifier obtained 84.96% classification accuracy with 50-50% train-test split for diagnosing the cardiac disorder from SPECT images, classifier ensemble based on AIRS and ICA fusion obtained 97.74% classification accuracy on the same conditions. The accuracy of AIRS classifier utilizing the reduced feature subsets was higher than those exploiting all the original features. These results show that the proposed ensemble method is very promising in diagnosis of the cardiac disorder from SPECT images.
Açıklama
18th International Conference on Database and Expert Systems Applications -- SEP 03-07, 2007 -- Univ Regensburg, Regensburg, GERMANY
Anahtar Kelimeler
Kaynak
DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
4653
