Abstract
Extraction of meaningful spectral signatures (sets of features) from high-dimensional biomedical datasets is an important stage of biomarker discovery. We present a novel feature extraction algorithm for supervised classification, based on the evidence accumulation framework, originally proposed by Fred and Jain for unsupervised clustering. By taking advantage of the randomness in genetic-algorithm-based feature extraction, we generate interpretable spectral signatures, which serve as hypotheses for corroboration by further research. As a benchmark, we used the state-of-the-art support vector machine classifier. Using external crossvalidation, we were able to obtain candidate biomarkers without sacrificing prediction accuracy.
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© 2005 Springer-Verlag Berlin Heidelberg
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Bamgbade, A., Somorjai, R., Dolenko, B., Pranckeviciene, E., Nikulin, A., Baumgartner, R. (2005). Evidence Accumulation to Identify Discriminatory Signatures in Biomedical Spectra. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_62
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DOI: https://doi.org/10.1007/11527770_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27831-3
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