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Finding a plan to improve recognition rate using classification analysis
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- 발행기관
- 국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
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- 간행물
- The International Journal of Advanced Smart Convergence KCI 등재 바로가기
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- 통권
- Volume 9 Number 4 (2020.12)바로가기
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- 페이지
- pp.184-191
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A387986
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. 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, the correlation between the variables, and multicollinearity. 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 according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.
목차
Abstract
1. Introduction
2. Definition of classification analysis
2.1 Machine Learning Technique
2.2 Decision Tree
2.3 Random Forest
3. Classification Analysis Experiment
3.1 Subject and Method of Experiment
3.2 Classification Analysis using Decision Tree
3.3 Classification Analysis using Random Forest
4. Conclusion
References
키워드
Machine Learning Decision Tee Random Forest Classification Recognition저자
간행물 정보
발행기관
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- 발행기관명
- 국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
- 설립연도
- 2000
- 분야
- 공학>전자/정보통신공학
- 소개
- 인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.
간행물
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- 간행물명
- The International Journal of Advanced Smart Convergence
- 간기
- 계간
- pISSN
- 2288-2847
- eISSN
- 2288-2855
- 수록기간
- 2012~2025
- 십진분류
- KDC 326 DDC 380
이 권호 내 다른 논문 / The International Journal of Advanced Smart Convergence Volume 9 Number 4
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