논문실적
| 수질자료의 이상치 탐색을 위한 Isolation Forest기법의 적용 |
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학술지명 대한환경공학회
저자 채선하,박노석,윤석민,김종은,윤상진
발표일 2018-12-31
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In this study, water quality dataset were collected from G_water treatment plants in South Korea and classified by the statistical correlation. For the groups with significant correlations, we compared and analyzed the outlier detection performance by applying distance and isolation forest techniques. For the group with insignificant correlation, we analyzed the outlier detection performance according to the change of machine learning instance after applying Isolation Forest method. As a result, the distance-based and Isolation Forest methods were able to effectively search global and local outliers in the water quality dataset. Furthermore Isolation Forest method is analyzed to search outliers in a wider range than the distance-based method. In the Isolation Forest method, the change of the outlier search performance according to the change of the machine learning amount is small. |
