A Novel Classification Method for Analysis of Multi-stage Diseases via Mass Spectrometric Data

Multi-category classification is one of the challenging issues in medical data analysis. We propose a new bi- classification algorithm for the multi-class classification, which is comprised of two schemes: error-correcting output coding (ECOC) and pairwise coupling (PWC). After fea- ture reduction i...

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Vydáno v:2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007) s. 237 - 244
Hlavní autoři: Oh, Jung Hun, Kim, Young Bun, Gao, Jean
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.01.2007
Témata:
Boosting
Decoding
Design methodology
Diseases
Error correction codes
Ionization
Mass spectroscopy
Support vector machine classification
Support vector machines
Testing
ISBN:0769530311, 9780769530314
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Shrnutí:Multi-category classification is one of the challenging issues in medical data analysis. We propose a new bi- classification algorithm for the multi-class classification, which is comprised of two schemes: error-correcting output coding (ECOC) and pairwise coupling (PWC). After fea- ture reduction in both schemes, each corresponding classi- fication strategy is performed. For a test sample, two class labels that are predicted in both schemes are compared. If two class labels are the same, we assign the test sample to an identical label; otherwise, only for samples belonging to different classes predicted from two schemes, a retraining method is employed. Our scheme is applied to the analysis of a MALDI-TOF data set which consists of hepatocellular carcinoma (HCC) patients, cirrhosis patients and healthy individuals. To validate the performance of our proposed algorithm, experiments were performed in comparison with other classification methods.
ISBN:0769530311
9780769530314
DOI:10.1109/BIBM.2007.50