Abstract
This paper describes and experimentally analyses a new dimension reduction method for microarray data. Microarrays, which allow simultaneous measurement of the level of expression of thousands of genes in a given situation (tissue, cell or time), produce data which poses particular machine-learning problems. The disproportion between the number of attributes (tens of thousands) and the number of examples (hundreds) requires a reduction in dimension. While gene/class mutual information is often used to filter the genes we propose an approach which takes into account gene-pair/class information. A gene selection heuristic based on this principle is proposed as well as an automatic feature-construction procedure forcing the learning algorithms to make use of these gene pairs. We report significant improvements in accuracy on several public microarray databases.
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Hanczar, B. (2005). Combining Feature Selection and Feature Construction to Improve Concept Learning for High Dimensional Data. In: Zucker, JD., Saitta, L. (eds) Abstraction, Reformulation and Approximation. SARA 2005. Lecture Notes in Computer Science(), vol 3607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527862_19
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DOI: https://doi.org/10.1007/11527862_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27872-6
Online ISBN: 978-3-540-31882-8
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