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Accuracy Estimation of a Classifier Based on the Differences in Samples
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- 발행기관
- 보안공학연구지원센터(IJSIP) 바로가기
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- 간행물
- International Journal of Signal Processing, Image Processing and Pattern Recognition 바로가기
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- 통권
- Vol.9 No.11 (2016.11)바로가기
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- 페이지
- pp.147-160
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A292864
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- The classification accuracy is an important standard to measure the quality of the classifier. Usually, the classification accuracy is assessed later, not during the classification process. Problems such as classification accuracy drops cannot be timely and effectively found. It is necessary that marking test samples when estimating classification accuracy. It is a problem that we care about that how much is the classification accuracy when a group of new samples obtained. The problem must be concerned when using and improving the classifier in the case of growing data. To solve this problem, this paper put forward different estimates from different perspectives which based on the difference between samples. One estimate is based on the difference in samples distribution, which is from the Bayesian criterion. Another estimate is based on the difference in each sample instance, which is from the K nearest neighbor classification. Classification accuracy is also estimated by using the artificial neural networks, which combine the characteristics of the above two methods. And results show the proposed methods have good effects.
목차
Abstract
1. Introduction
2. Classification Accuracy Estimation Based on the Differences in Samples Distribution
2.1. MMD Statistics
2.2. MMD Statistitcs and Classification Accuracy
3. Classification Accuracy Estimation Based on the Differences in Samples Instances
3.1. MMR Statistics
3.2. MMR Statistics and Classification Accuracy
4. Classification Accuracy Estimation Based on the MMD and MMR
5. Experiments and Analysis
5.1. Experimental Settings
5.2. Experiment One: Samples Difference, MMD and MMR
5.3. Experiment Two: SamplesSize, MMD and MMR
5.4. Experiment Three: Test Accuracy Estimation
6. Conclusion
References
키워드
Classification accuracy Samples distribution Bayesian criterion K nearest neighbor classification Artificial neural networks저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJSIP) [Science & Engineering Research Support Center, Republic of Korea(IJSIP)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Signal Processing, Image Processing and Pattern Recognition
- 간기
- 격월간
- pISSN
- 2005-4254
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.9 No.11
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