Conference Paper

An Improved FMM Neural Network for Classification of Gene Expression Data

DOI: 10.1007/978-3-540-71441-5_8 Conference: Fuzzy Information and Engineering, Proceedings of the Second International Conference of Fuzzy Information and Engineering, ICFIE 2007, May 13-16, 2007, Guangzhou, China
Source: DBLP


Gene microarray experiment can monitor the expression of thousands of genes simultaneously. Using the promising technology,
accurate classification of tumor subtypes becomes possible, allowing for specific treatment that maximizes efficacy and minimizes
toxicity. Meanwhile, optimal genes selected from microarray data will contribute to diagnostic and prognostic of tumors in
low cost. In this paper, we propose an improved FMM (fuzzy Min-Max) neural network classifier which provides higher performance
than the original one. The improved one can automatically reduce redundant hyperboxes thus it can solve difficulty of setting
the parameter θ value and is able to select discriminating genes. Finally we apply our improved classifier on the small, round blue-cell
tumors dataset and get good results.

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