Abstract
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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Juan, L., Fei, L., Yongqiong, Z. (2007). An Improved FMM Neural Network for Classification of Gene Expression Data. In: Cao, BY. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71441-5_8
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DOI: https://doi.org/10.1007/978-3-540-71441-5_8
Publisher Name: Springer, Berlin, Heidelberg
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