Optimasi Klasifikasi Bayesian Network Melalui Reduksi Attribute Menggunakan Metode Principal Component Analysis

Authors

  • Surizar Rahmi STMIK Mikroskil, Medan
  • Pahala Sirait STMIK Mikroskil, Medan
  • Erwin Setiawan Panjaitan STMIK Mikroskil, Medan

DOI:

https://doi.org/10.30865/mib.v4i4.2370

Keywords:

Reduction, Attribute, Principal Component Analysis, Bayesian Network Classification

Abstract

Dimensionality reduction is a hot topic being discussed in its development has been carried out in various fields of research one of which is machine learning by reducing can reduce the capacity of dimensions without reducing (eliminating) information contained in the data. Principal Component Analysis is one of the proven reduction techniques capable of reducing data capacity without significantly eliminating the information contained in the dataset. In this research attribute reduction using principal component analysis using a dataset of factors affecting employee absence was taken from the University of California repository at Irvine (UCI). Combination with Bayesian Network to classify data as a comparison between before and after attribute reduction. This can be seen in the initial results before the reduction with an accuracy of 100% and after the fifth attribute reduction there is a decrease in accuracy by 89,7%

Author Biographies

Surizar Rahmi, STMIK Mikroskil, Medan

Program Studi Magister Teknologi Informasi

Pahala Sirait, STMIK Mikroskil, Medan

Program Studi Magister Teknologi Informasi

Erwin Setiawan Panjaitan, STMIK Mikroskil, Medan

Program Studi Magister Teknologi Informasi

References

Y. Luo, K. Li, Y. Li, D. Cai, C. Zhao, and Q. Meng, “Three-Layer Bayesian Network for Classification of Complex Power Quality Disturbances,†IEEE Trans. Ind. Informatics, vol. 14, no. 9, pp. 3997–4006, 2018, doi: 10.1109/TII.2017.2785321.

M. Habib, V. Chang, A. Batool, and T. Ying, “International Journal of Information Management Big data reduction framework for value creation in sustainable enterprises,†Int. J. Inf. Manage., vol. 36, no. 6, pp. 917–928, 2016, doi: 10.1016/j.ijinfomgt.2016.05.013.

S. Ramírez-Gallego, B. Krawczyk, S. García, M. Woźniak, and F. Herrera, “A survey on data preprocessing for data stream mining: Current status and future directions,†Neurocomputing, vol. 239, pp. 39–57, 2017, doi: 10.1016/j.neucom.2017.01.078.

A. A. Yildirim, C. Özdoğan, and D. Watson, “Parallel data reduction techniques for big datasets,†Big Data Manag. Technol. Appl., no. December 2015, pp. 72–93, 2013, doi: 10.4018/978-1-4666-4699-5.ch004.

L. Shiyue, Y. Dong, D. Song, and Z. Liping, “Data filtering algorithm based on attribute reduction and gene expression programming,†2018 IEEE 3rd Int. Conf. Big Data Anal. ICBDA 2018, pp. 248–253, 2018, doi: 10.1109/ICBDA.2018.8367686.

N. B. Shah, K. Lee, and K. Ramchandran, “When Do Redundant Requests Reduce Latency?,†IEEE Trans. Commun., vol. 64, no. 2, pp. 715–722, 2016, doi: 10.1109/TCOMM.2015.2506161.

I. T. Jollife and J. Cadima, “Principal component analysis: A review and recent developments,†Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., vol. 374, no. 2065, 2016, doi: 10.1098/rsta.2015.0202.

K. J. Galinsky et al., “Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia,†Am. J. Hum. Genet., vol. 98, no. 3, pp. 456–472, 2016, doi: 10.1016/j.ajhg.2015.12.022.

Y. Aït-Sahalia and D. Xiu, “Principal Component Analysis of High-Frequency Data,†J. Am. Stat. Assoc., vol. 114, no. 525, pp. 287–303, 2019, doi: 10.1080/01621459.2017.1401542.

C. Lu, J. Feng, Y. Chen, W. Liu, Z. Lin, and S. Yan, “Lu_Tensor_Robust_Principal_CVPR_2016_paper.pdf,†pp. 5249–5257.

T. Metsalu and J. Vilo, “ClustVis: A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap,†Nucleic Acids Res., vol. 43, no. W1, pp. W566–W570, 2015, doi: 10.1093/nar/gkv468.

S. Yi, Z. Lai, Z. He, Y. ming Cheung, and Y. Liu, “Joint sparse principal component analysis,†Pattern Recognit., vol. 61, pp. 524–536, 2017, doi: 10.1016/j.patcog.2016.08.025.

Z. Zhao, Y. Shkolnisky, and A. Singer, “Fast Steerable Principal Component Analysis,†IEEE Trans. Comput. Imaging, vol. 2, no. 1, pp. 1–12, 2016, doi: 10.1109/tci.2016.2514700.

J. Alonso-Gutierrez et al., “Principal component analysis of proteomics (PCAP) as a tool to direct metabolic engineering,†Metab. Eng., vol. 28, pp. 123–133, 2015, doi: 10.1016/j.ymben.2014.11.011.

S. H. Wang et al., “Multiple Sclerosis Detection Based on Biorthogonal Wavelet Transform, RBF Kernel Principal Component Analysis, and Logistic Regression,†IEEE Access, vol. 4, pp. 7567–7576, 2016, doi: 10.1109/ACCESS.2016.2620996.

C. A. Magee, P. Caputi, and J. K. Lee, “Distinct longitudinal patterns of absenteeism and their antecedents in full-time australian employees,†J. Occup. Health Psychol., vol. 21, no. 1, pp. 24–36, 2016, doi: 10.1037/a0039138.

A. C. Constantinou, N. Fenton, W. Marsh, and L. Radlinski, “From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support,†Artif. Intell. Med., vol. 67, pp. 75–93, 2016, doi: 10.1016/j.artmed.2016.01.002.

S. C. Ng, “Principal component analysis to reduce dimension on digital image,†Procedia Comput. Sci., vol. 111, pp. 113–119, 2017, doi: 10.1016/j.procs.2017.06.017.

D. Ballabio, R. Todeschini, and V. Consonni, Recent Advances in High-Level Fusion Methods to Classify Multiple Analytical Chemical Data, vol. 31. Elsevier, 2019.

Y. You, J. Li, and N. Xu, “A constrained parameter evolutionary learning algorithm for Bayesian network under incomplete and small data,†Chinese Control Conf. CCC, pp. 3044–3051, 2017, doi: 10.23919/ChiCC.2017.8027825.

J. Lee, R. Henning, and M. Cherniack, “Correction workers’ burnout and outcomes: A bayesian network approach,†Int. J. Environ. Res. Public Health, vol. 16, no. 2, 2019, doi: 10.3390/ijerph16020282.

Downloads

Published

2020-10-20

Issue

Vol. 4 No. 4 (2020): Oktober 2020

Section

Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).