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The Speed and Accuracy Evaluation of Random Forest Performance by Selecting Features in the Transformation Data
Prasetiyowati M.I.a, Maulidevi N.U.a, Surendro K.a
a Bandung Institute of Technology, Jawa Barat, 40116, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 ACM.Random Forest is a machine learning method by building several trees in a forest, and getting the results of the classification by voting. The method of taking features to build a tree is done randomly, so there is a possibility that the feature chosen is not necessarily informative. Feature selection is needed to speed up the process. The feature selection used in this study is Correlation-based Feature Selection with the best first method. Based on the results of trials using six high-dimensional datasets, it was found that the selected features decreased by 15% to 96%. The average time needed to execute a Random Forest is less than that of a Random Forest execution on a dataset that has not been selected for features. This applies to datasets that have been transformed using Fast Fourier Transform, and returned using the Inverse Fast Fourier Transform. The average accuracy value for the dataset that has been transformed, accuracy has increased 0.03 to 0.08% compared to the dataset that has not been transformed. FFT is used to test the performace enhancement of the tranformed data of the Radom Forest.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Accuracy evaluation,High dimensional datasets,Inverse fast Fourier transforms,Machine learning methods,Speed up[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Accuracy,Correlation-based Feature Selection,Fast Fourier Transform,Inverse Fast Fourier Transform,Random Forest,Speed[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This study and research has been financially supported by Universitas Multimedia Nusantara (UMN).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1145/3386762.3386768[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]