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Hybrid Parallel Feature Subset Selection for High Dimensional Datasets
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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.
Title: Hybrid Parallel Feature Subset Selection for High Dimensional Datasets
Description:
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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