Abstract
In Supervised Learning it is assumed that is straightforward to obtained labeled data. However, in reality labeled data can be scarce or expensive to obtain. Active Learning (AL) is a way to deal with the above problem by asking for the labels of the most “informative” data points. We propose a novel AL method based on wavelet analysis, which pertains especially to the large number of dimensions (i.e. examined genes) of microarray experiments. DNA Microarray expression experiments permit the systematic study of the correlation of the expression of thousands of genes. We have applied our method on such data sets with encouraging results. In particular we studied data sets concerning: Small Round Blue Cell Tumours (4 types), Leukemia (2 types) and Lung Cancer (2 types).
The work presented in this paper has been undertaken in the framework of the OPTOPOIHSH project (PLHRO/0104/04 – Development of knowledge-based Visual Attention models for Perceptual Video Coding) funded by the Cyprus Research Promotion Foundation, Framework Programme for Research and Technological Development 2003-2005.
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Vogiatzis, D., Tsapatsoulis, N. (2006). Active Learning with Wavelets for Microarray Data. In: Bloch, I., Petrosino, A., Tettamanzi, A.G.B. (eds) Fuzzy Logic and Applications. WILF 2005. Lecture Notes in Computer Science(), vol 3849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11676935_31
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DOI: https://doi.org/10.1007/11676935_31
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