Anomaly detection model based on multi-grained cascade isolation forest algorithm
The isolation-based anomaly detector,isolation forest has two weaknesses,its inability to detect anomalies that were masked by axis-parallel clusters,and anomalies in high-dimensional data.An isolation mechanism based on random hyperplane and a multi-grained scanning was proposed to overcome these w...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-08-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019132/ |
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| author | Xiaohui YANG Shengchang ZHANG |
| author_facet | Xiaohui YANG Shengchang ZHANG |
| author_sort | Xiaohui YANG |
| collection | DOAJ |
| description | The isolation-based anomaly detector,isolation forest has two weaknesses,its inability to detect anomalies that were masked by axis-parallel clusters,and anomalies in high-dimensional data.An isolation mechanism based on random hyperplane and a multi-grained scanning was proposed to overcome these weaknesses.The random hyperplane generated by a linear combination of multiple dimensions was used to simplify the isolation boundary of the data model which was a random linear classifier that can detect more complex data patterns,so that the isolation mechanism was more consistent with data distribution characteristics.The multi-grained scanning was used to perform dimensional sub-sampling which trained multiple forests to generate a hierarchical ensemble anomaly detection model.Experiments show that the improved isolation forest has better robustness to different data patterns and improves the efficiency of anomaly points in high-dimensional data. |
| format | Article |
| id | doaj-art-135353fe4aa9422983a0bddfafb5f38b |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-08-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-135353fe4aa9422983a0bddfafb5f38b2025-01-14T07:17:33ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-08-014013314259729111Anomaly detection model based on multi-grained cascade isolation forest algorithmXiaohui YANGShengchang ZHANGThe isolation-based anomaly detector,isolation forest has two weaknesses,its inability to detect anomalies that were masked by axis-parallel clusters,and anomalies in high-dimensional data.An isolation mechanism based on random hyperplane and a multi-grained scanning was proposed to overcome these weaknesses.The random hyperplane generated by a linear combination of multiple dimensions was used to simplify the isolation boundary of the data model which was a random linear classifier that can detect more complex data patterns,so that the isolation mechanism was more consistent with data distribution characteristics.The multi-grained scanning was used to perform dimensional sub-sampling which trained multiple forests to generate a hierarchical ensemble anomaly detection model.Experiments show that the improved isolation forest has better robustness to different data patterns and improves the efficiency of anomaly points in high-dimensional data.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019132/anomaly detectionisolation forestisolation mechanismmulti-grained scanningrandom hyperplane |
| spellingShingle | Xiaohui YANG Shengchang ZHANG Anomaly detection model based on multi-grained cascade isolation forest algorithm Tongxin xuebao anomaly detection isolation forest isolation mechanism multi-grained scanning random hyperplane |
| title | Anomaly detection model based on multi-grained cascade isolation forest algorithm |
| title_full | Anomaly detection model based on multi-grained cascade isolation forest algorithm |
| title_fullStr | Anomaly detection model based on multi-grained cascade isolation forest algorithm |
| title_full_unstemmed | Anomaly detection model based on multi-grained cascade isolation forest algorithm |
| title_short | Anomaly detection model based on multi-grained cascade isolation forest algorithm |
| title_sort | anomaly detection model based on multi grained cascade isolation forest algorithm |
| topic | anomaly detection isolation forest isolation mechanism multi-grained scanning random hyperplane |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019132/ |
| work_keys_str_mv | AT xiaohuiyang anomalydetectionmodelbasedonmultigrainedcascadeisolationforestalgorithm AT shengchangzhang anomalydetectionmodelbasedonmultigrainedcascadeisolationforestalgorithm |