Earticle

초록

영어

It is crucial to develop effective and efficient big data analytics methods for problem solving in the field of business in order to improve the performance of data analytics and reduce costs and risks in the analysis of customer data. In this study, a big data-driven data analysis system using artificial intelligence techniques is designed to increase the accuracy of big data analytics along with the rapid growth of the field of data science. We present a key direction for big data analysis systems through missing value imputation, outlier detection, feature extraction, utilization of explainable artificial intelligence techniques, and exploratory data analysis. Our objective is not only to develop big data analysis techniques with complex structures of business data but also to bridge the gap between the theoretical ideas in artificial intelligence methods and the analysis of real-world data in the field of business.

목차

Abstract
1. Introduction
2. Method
2.1 Missing Data Imputation
2.2 Outlier detection
2.3 Feature Selection: GLASSO
2.4 Explainable Artificial Intelligence Techniques: GLMNET
2.5 Data
3. Performance of Statistical Pre-Processing
3.1 Missing Data Imputation
3.2 Outlier detection
3.3 Feature Extraction and Network Analysis
4. Performance of Penalized Regression
5. Discussion
Acknowledgments
References

키워드

저자

• Donggeun Kim [ Department of Big Data Science, College of Public Policy, Korea University ]
• Sangjin Kim [ Department of National Statistics, College of Public Policy, Korea University ]
• Juyong Ko [ Department of Big Data Science, College of Public Policy, Korea University ]
• Jai Woo Lee [ Department of Big Data Science, College of Public Policy, Korea University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658