@inproceedings{8cb8907694bb4d29adb6062b3672abcf,
title = "2-Dimensional homogeneous distributed ensemble feature selection",
abstract = "A bstract—Big data can be seen from the number of its samples and features. The selection of the most representative feature is an important task in big data analysis to reduce the dimension. The feature selection method is used to handle this problem. In this research, a homogeneous distributed ensemble feature selection method with 2-dimensional partition is used as new approach of feature selection. The results showed that the proposed method can improve the accuracy from the other feature selection method with an increase of 2% for several datasets. In addition, it also speeds up the computation to almost two times faster.",
keywords = "2D Ensem ble, Big data, Distributed, Feature selection, Hom ogeneous",
author = "Alhamidi, {Machmud R.} and Arsa, {Dewa M.S.} and Rachmadi, {Muhammad Febrian} and Wisnu Jatmiko",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE. All Rights Reserved.; 10th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018 ; Conference date: 27-10-2018 Through 28-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ICACSIS.2018.8618266",
language = "English",
series = "2018 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "367--372",
booktitle = "2018 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018",
address = "United States",
}