Feature selection based on correlations of gene-expression values for cancer prediction
Conference proceedings article
ผู้เขียน/บรรณาธิการ
กลุ่มสาขาการวิจัยเชิงกลยุทธ์
ไม่พบข้อมูลที่เกี่ยวข้อง
รายละเอียดสำหรับงานพิมพ์
รายชื่อผู้แต่ง: Pattanateepapon A., Suwansantisuk W., Kumhom P.
ผู้เผยแพร่: Hindawi
ปีที่เผยแพร่ (ค.ศ.): 2016
หน้าแรก: 716
หน้าสุดท้าย: 721
จำนวนหน้า: 6
ISBN: 9781467377911
นอก: 0146-9428
eISSN: 1745-4557
ภาษา: English-Great Britain (EN-GB)
บทคัดย่อ
Microarray-based cancer prediction can save lives, but is difficult to perform accurately due to noise, outliers, and inherent anomalies in gene-expression measurements. One important method to improve accuracy, sensitivity, and specificity of cancer prediction is feature selection, where only relevant genes participate in classification. Current features-selection methods normally have high computational-cost or require trial and error to find a suitable parameter. We develop a feature-selection method called the support-vector-machine recursive feature-elimination (SVMRFE) with minimum Pearson correlation. This method selects genes that together classify well the samples and are weakly correlated, meaning that the selected genes are both relevant for classification and small in number. The proposed method preprocesses microarray data and adjusts automatically and adaptively a parameter to produce high classification performance. We compare the proposed method with some existing ones in terms of accuracy, sensitivity, and specificity. The results show that the proposed method outperforms and ties with a close competitor, the SVMRFE with maximum relevance and minimum redundancy, in 45 out of 72 test cases. In addition, the proposed method outperforms other four classic baselines in 3 out of 6 test cases. The proposed method of feature selection is easy to implement, systematic, and has practical applications to bioinformatics. ฉ 2016 IEEE.
คำสำคัญ
minimum redundancy and maximum relevance
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