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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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Classification algorithms generally assume that the data is complete. However, missing values are common in real data sets due to various reasons. In this paper, we propose to use locally linear reconstruction (LLR) for missing value imputation to improve the classification performance when missing values exist. We first investigate how much missing values degenerate the classification performance with regard to various missing ratios. Then, we compare the proposed missing value imputation (LLR) with three well-known single imputation methods over three different classifiers using eight data sets. The experimental results showed that (1) any imputation methods, although some of them are very simple, helped to improve the classification accuracy; (2) among the imputation methods, the proposed LLR imputation was the most effective over all missing ratios, and (3) when the missing ratio is relatively high, LLR was outstanding and its classification accuracy was as high as the classification accuracy derived from the compete data set.
목차
1. 서론
2. 지역적 선형 재구축을 통한 결측치 대치
3. 실험 설계
4. 실험 결과 및 토의
5. 결론 및 향후 계획
참고문헌
참고문헌 (25)
참고문헌 신청
1. [학술대회논문] - Acuna, E. / 2004 / The Treatment of Missing Values and Its Effect in the Classifier Accuracy, in Classification / Clustering and Data Mining Applications : 639 ~ 648
2. [학술지(정기간행물)] - Batista, G. E. A. P. A. / 2003 / An Analysis of Four Missing Data Treatment Methods for Supervosed Learning / Applied Artificial Intelligence 17 (5-6) : 519 ~ 533
3. [학술지(정기간행물)] - Bernard, J. / 1999 / Applications of Multiple Imputation in Medical Studies : From AIDS to NHANES / Statistical Methods in Medical Research 8 (1) : 17 ~ 36
4. [단행본] - Bishop, C. M. / 2006 / Pattern Recognition and Machine Learning / Springer
5. [단행본] - Breiman, L. / 1984 / Classification and Regression Trees / CRC Press
2. [학술지(정기간행물)] - Batista, G. E. A. P. A. / 2003 / An Analysis of Four Missing Data Treatment Methods for Supervosed Learning / Applied Artificial Intelligence 17 (5-6) : 519 ~ 533
3. [학술지(정기간행물)] - Bernard, J. / 1999 / Applications of Multiple Imputation in Medical Studies : From AIDS to NHANES / Statistical Methods in Medical Research 8 (1) : 17 ~ 36
4. [단행본] - Bishop, C. M. / 2006 / Pattern Recognition and Machine Learning / Springer
5. [단행본] - Breiman, L. / 1984 / Classification and Regression Trees / CRC Press
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UCI(KEPA) : I410-ECN-0101-2014-530-000855625