Abstract
This paper presents a novel approach for identifying relevant genes by employing a fuzzy classifier. First a fuzzy classifier rule set is derived such that each rule involves a compact set of genes. Then, a correlation matrix is produced by considering the correlations between the genes in each rule. Apriori is applied on the correlation matrix to find the maximal sets of correlated genes after tuning the minimum support value. Experiments conducted on the Leukemia dataset demonstrate the effectiveness of the proposed approach in producing relevant genes.
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Khabbaz, M., Kianmher, K., Alshalalfa, M., Alhajj, R. (2007). Fuzzy Classifier Based Feature Reduction for Better Gene Selection. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_31
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DOI: https://doi.org/10.1007/978-3-540-74553-2_31
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