Abstract
High-resolution spectroscopy is a powerful industrial tool. The number of features (wavelengths) in these data sets varies from several hundreds up to a thousand. Relevant feature selection/extraction algorithms are necessary to handle data of such a large dimensionality. One of the possible solutions is the SVM shaving technique. It was developed for applications in microarray data, which also have a huge number of features. The fact that the neighboring features (wavelengths) are highly correlated allows one to propose the SVM band-shaving algorithm, which takes into account the prior knowledge on the wavelengths order. The SVM band-shaving has a lower computational demands than the standard SVM shaving and selects features organized into bands. This is preferable due to possible noise reduction and a more clear physical interpretation.
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Keywords
- Feature Selection Method
- Band Extraction
- Lower Computational Demand
- Total Classification Error
- High Dimensional Microarray Data
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© 2004 Springer-Verlag Berlin Heidelberg
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Verzakov, S., PaclĂk, P., Duin, R.P.W. (2004). Feature Shaving for Spectroscopic Data. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_113
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DOI: https://doi.org/10.1007/978-3-540-27868-9_113
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