Affiliations: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt
Correspondence:
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Corresponding author. Mohamed E. Abd Elaziz, Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt. E-mails: [email protected]; [email protected].
Abstract: The microarray data are important to detect diseases, however, there are a large number of genes with small sample size, and this leads to slow convergence speed and reducing the prediction accuracy. Therefore, reducing the dimension of data is needed as preprocessing step for classification of data. There are two methods can be used to perform the dimension reduction, namely, the feature extraction and feature selection. The feature extraction methods are transforming data into another space and then a subset of features are selected using some criteria. The projection of the measurements, using these methods, is different from the original data. Unlike feature extraction, the feature selection methods select relevant features without changing their values, however, these methods need a large time than feature extraction. There are some algorithms can simultaneously select and extract features from data to take the advantages of both methods. This paper proposed a new simultaneous feature extraction/selection method for high-dimensional microarray data. The proposed method combines fuzzy neighborhood rough set method with nonnegative matrix factorization based on multiobjective evolutionary. To evaluate the accuracy of our approach, a computational experiments were performed on seven gene microarray datasets with diverse characteristics. Experimental results illustrate that the proposed method is better than other algorithms in term of performance measures.
Keywords: Principal component analysis (PCA), independent component analysis (ICA), nonnegative matrix factorization (NMF) and fuzzy neighborhood rough set (FNRS)
