Cancer is currently one of the most threatening diseases. The analysis of high dimensional microarray data is a method that is frequently used for cancer diagnosis. Multi category imbalance data analysis in the medical sciences plays crucial role. The growing number of cancer patients has made the necessity for cancer classification absolutely necessary. In this study, PE-SU-R-CNN and PE-ChS-R-CNN are employed as parallel ensemble feature selection based deep learning classifiers to address issues for multi-class cancer classification. The deep learning convolutional neural network with soft-max activation function at decision layer is used. The most well known nine cancer microarray gene expression datasets are used in the experiments. The proposed system's empirical findings are compared with deep learning techniques based on bio-inspired feature selection firefly and elephant searches. Finally, a one-way ANOVA statistical significance test with a post hoc Tukey's test is used to draw several inferences about the optimal classifier model to use. The proposed parallel model shows significant improvement over existing methods.
High dimensional data analytics is emerging research field in this digital world. The gene expression microarray data, remote sensor data, medical data, image, video data are some of the examples of high dimensional data. Feature subset selection is challenging task for such data. To achieve diversity and accuracy with high dimensional data is important aspect of this research. To reduce time complexity parallel stepwise feature subset selection approach is adopted for feature subset selection in this paper. Our aim is to reduce time complexity and enhancing the classification accuracy with minimum number of selected feature subset. With this approach 88.18% average accuracy is achieved.
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