Earticle

초록

영어

With the recent introduction of artificial intelligence (AI) technology, the use of data is rapidly increasing, and newly generated data is also rapidly increasing. In order to obtain the results to be analyzed based on these data, the first thing to do is to classify the data well. However, when classifying data, if only one classification technique belonging to the machine learning technique is applied to classify and analyze it, an error of overfitting can be accompanied. In order to reduce or minimize the problems caused by misclassification of the classification system such as overfitting, it is necessary to derive an optimal classification by comparing the results of each classification by applying several classification techniques. If you try to interpret the data with only one classification technique, you will have poor reasoning and poor predictions of results. This study seeks to find a method for optimally classifying data by looking at data from various perspectives and applying various classification techniques such as LDA and QDA, such as linear or nonlinear classification, as a process before data analysis in data analysis. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable and the correlation between the variables. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified to suit the purpose of analysis. This is a process that must be performed before reaching the result by analyzing the data, and it may be a method of optimal data classification.

목차

Abstract
1. Introduction
2. Machine Learning Definition
3. Data Mining Definition
3.1. Features of Data Mining
3.2. Discriminant Analysis
3.3. Analysis Method of Discriminant Analysis
3.4. Analysis stage of Discriminant Analysis
3.5. Classification Model Verification of DA
4. Discriminant Analysis Experiment
4.1. Subject and Method of Experiment
4.2. Discriminant Analysis Using LDA
4.3. Discriminant Analysis Using QDA
5. Conclusion
References

키워드

저자

• SeungJae Kim [ Department of Convergence, Honam University, Gwangju ]
• SungHwan Kim [ National Program of Excellence in Software center, Chosun University, Gwangju ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  통합자연과학논문집(구 조선자연과학논문집) [Journal of Integrative Natural Science]

• 간기

  계간

• pISSN

  2005-1042

• 수록기간

  2008~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 405 DDC 505