Loading...

A novel ensemble strategy for classification of prostate cancer protein mass spectra

Assareh, A ; Sharif University of Technology | 2007

507 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/IEMBS.2007.4353712
  3. Publisher: 2007
  4. Abstract:
  5. Protein mass spectra pattern recognition is a new forum in which many machine learning algorithms have been conducted to enhance the chance of early cancer diagnosis. The high-dimensionality-small-sample (HDSS) problem of cancer proteomic datasets still requires more sophisticated approaches to improve the classification accuracy. In this study we present a simple ensemble strategy based on measuring the generalizing capability of different subsets of training data and apply it in making final decision. Using a limited number of biomarkers along with 5 classification algorithms, the proposed method achieved a promising performance over a well-known prostate cancer mass spectroscopy dataset. © 2007 IEEE
  6. Keywords:
  7. Biomarkers ; Classification (of information) ; Learning systems ; Mass spectrometers ; Proteins ; Cancer proteomic datasets ; Classification algorithms ; Prostate cancer mass spectroscopy dataset ; Oncology ; High dimensionality small sample (HDSS)
  8. Source: 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, 23 August 2007 through 26 August 2007 ; 2007 , Pages 5987-5990 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4353712