دورية أكاديمية
A Novel PSO-FLANN Framework of Feature Selection and Classification for Microarray Data.
| العنوان: | A Novel PSO-FLANN Framework of Feature Selection and Classification for Microarray Data. |
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| المؤلفون: | Parhi, Pournamasi, Mishra, Debahuti, Mishra, Sashikala, Shaw, Kailash |
| المصدر: | Procedia Engineering; Sep2012, Vol. 38, p1644-1649, 6p |
| مستخلص: | Abstract: Feature selection is a method of finding appropriate features from a given dataset. Last few years a number of feature selection methods have been proposed for handling the curse of dimensionality with microarray data set. Proposed framework has used two feature selection methods: Principal component analysis (PCA) and Factor analysis (FA). Typically microarray data contains number of genes with huge number of conditions. In such case there is a need of good classifier to classify the data. In this paper, particle swarm optimization (PSO) is used for classification because the parameters of PSO can be optimized for a given problem. In recent years PSO has been used increasingly as a novel technique for solving complex problems. To classify the microarray dataset, the functional link artificial neural network (FLANN) used the PSO to tune the parameters of FLANN. This PSO-FLANN classifier has been used to classify three different microarray data sets to achieve the accuracy. The proposed PSO-FLANN model has also been compared with discriminant Analysis (DA). Experiments were performed on the three microarray datasets and the simulation shows that PSO-FLANN gives more than 80% accuracy. [Copyright &y& Elsevier] |
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| قاعدة البيانات: | Supplemental Index |
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