참고문헌 (21)

1. Chawla, N. V., Bowyer, K. W., Hall, L. O., Kegelmeyer, W. P.. SMOTE: Synthetic Minority Over-sampling Technique. The journal of artificial intelligence research, vol.16, 321-357.

   상세보기

2. Cho, Eunnuri, Chang, Tai-Woo, Hwang, Gyusun. Data Preprocessing Combination to Improve the Performance of Quality Classification in the Manufacturing Process. Electronics, vol.11, no.3, 477-.

   상세보기

3. Dua, D. and Graff, C. (2019), UCI Machine Learning Repository, Irvine, CA. 

4. Escobar, Carlos A., McGovern, Megan E., Morales-Menendez, Ruben. Quality 4.0: a review of big data challenges in manufacturing. Journal of intelligent manufacturing, vol.32, no.8, 2319-2334.

   상세보기

5. García-Laencina, Pedro J., Abreu, Pedro Henriques, Abreu, Miguel Henriques, Afonoso, Noémia. Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values. Computers in biology and medicine, vol.59, 125-133.

   상세보기

6. Graham, John W.. Missing Data Analysis: Making It Work in the Real World. Annual review of psychology, vol.60, 549-576.

   상세보기

7. Hall, D.L., Llinas, J.. An introduction to multisensor data fusion. Proceedings of the IEEE, vol.85, no.1, 6-23.

   상세보기

8. 전성해. Support Vector Regression을 이용한 이상치 데이터분석. 한국지능시스템학회 논문지 = Journal of Korean institute of intelligent systems, vol.18, no.6, 876-880.

   원문보기 상세보기

9. Kim, J. H. and Kwahk, K. Y. (2022), Class Imbalance Resolution Method and Classification Algorithm Suggesting Based on Dataset Type Segmentation, Journal of Intelligence and Information System, 28(3), 23-43. 

10. Kim, S. D., Cho, H. C., Kim, C. Y., Yang, T. H., and Kim S. H. (2018), Publish-Subscribe Model Based Efficient Data Acquisition Architecture for Industrial Wireless Sensor Networks, Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, 1336-1337. 

11. Krishnamurthi, Rajalakshmi, Kumar, Adarsh, Gopinathan, Dhanalekshmi, Nayyar, Anand, Qureshi, Basit. An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques. Sensors, vol.20, no.21, 6076-.

    상세보기

12. Kwak, Sang Kyu, Kim, Jong Hae. Statistical data preparation: management of missing values and outliers. Korean journal of anesthesiology, vol.70, no.4, 407-411.

    상세보기

13. Lee, Justin Y., Styczynski, Mark P.. NS-kNN: a modified k-nearest neighbors approach for imputing metabolomics data. Metabolomics : official journal of the Metabolomics Society, vol.14, no.12, 153-.

    상세보기

14. Lyle, M. (2017), From Paper and Pencil to Industry 4.0: Revealing the Value of Data Through Quality Intelligence, Quality Magazine, 56(10), 25-29. 

15. Micci-Barreca, Daniele. A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems. SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowlege Discovery & Data Mining, vol.3, no.1, 27-32.

    상세보기

16. Ministry of SMEs and Startups of Korea (2020), Molding AI Dataset, Korea AI Manufacturing Platform (KAMP), Available at: https://kamp-ai.kr. 

17. Ng, A. (2021), A chat with Andrew on MLOps: From Model-centric to Data-centric AI, Available on-line: https://www.youtube.com/watch?v=06-AZXmwHjot=1607s. 

18. Scheffer, J. (2002), Dealing with Missing Data, Research Letters in the Information and Mathematical Sciences, 3(1), 153-160. 

19. Schmitt, P., Mandel, J., and Guedj, M. (2015), A Comparison of Six Methods for Missing Data Imputation, Journal of Biometrics and Biostatistics, 6(1), 1-6. 

20. Shah, Jasmit S., Rai, Shesh N., DeFilippis, Andrew P., Hill, Bradford G., Bhatnagar, Aruni, Brock, Guy N.. Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies. BMC bioinformatics, vol.18, no.1, 114-.

    상세보기

21. Zhang, Xueying, Li, Ruixian, Zhang, Bo, Yang, Yunxiang, Guo, Jing, Ji, Xiang. An instance-based learning recommendation algorithm of imbalance handling methods. Applied mathematics and computation, vol.351, 204-218.

    상세보기