کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
385975 | 660876 | 2011 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel support vector sampling technique to improve classification accuracy and to identify key genes of leukaemia and prostate cancers
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
By extracting significant samples (which we refer to as support vector samples as they are located only on support vectors), we can identify principal genes and then use these genes to classify cancers either by support vector machines (SVM) or back-propagation neural networking (BPNN). We call this approach the support vector sampling technique (SVST). No matter the number of genes selected, our SVST method shows a significant improvement of classification performance. Our SVST method has averages 2–3% better performance when applied to leukemia and 6–7% better performance when applied to prostate cancer.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 38, Issue 4, April 2011, Pages 3209–3219
Journal: Expert Systems with Applications - Volume 38, Issue 4, April 2011, Pages 3209–3219
نویسندگان
Austin H. Chen, Ching-Heng Lin,