This repository contains data and codes to reproduce results of the conference paper titled "Feature subset selection using sparse principal component analysis and multiclass classification using selected features". The paper was presented at "32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management 2019 (COMADEM 2019)".
Codes are written in R and we have run it on R-3.5.3. The code will save some figures and tables in local directory. Some of those figures have been used in the paper. IMS bearing data have been used in this paper. We have extracted features from the original data. These feature matrices can be downloaded and used in the code.
Base R : 3.5.3 (MRO 3.5.3 can also be used)
e1071 : 1.7-2
ggplot2 : 3.0.0
lars : 1.2
elasticnet : 1.1.1
If these packages are not already installed, command install.packages("package_name") can be used to install new packages.
For other reproducible results on condition monitoring, readers can visit my project page on my personal website.
Cite this work as:
@incollection{Sahoo_2020,
doi = {10.1007/978-3-030-57745-2_13},
url = {https://doi.org/10.1007%2F978-3-030-57745-2_13},
year = 2020,
publisher = {Springer International Publishing},
pages = {147--158},
author = {Biswajit Sahoo and A. R. Mohanty},
title = {Feature Subset Selection Using Sparse Principal Component Analysis and Multiclass Fault Classification Using Selected Features},
booktitle = {Advances in Asset Management and Condition Monitoring}
}