An Optimized Computational Framework for Isolation Forest

An Optimized Computational Framework for Isolation Forest

Isolation Forest or iForest is one of the outstanding outlier detectors proposed in recent years. Yet, in the model setting, it is mainly based on the technique of randomization and, as a result, it is not clear how to select a proper attribute and how to locate an optimized split point on a given a...

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Main Authors: Zhen Liu, Xin Liu, Jin Ma, Hui Gao
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2018/2318763
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spelling doaj-e3acfbf273674234a661467fb2e6b54f2020-11-24T23:18:30ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/23187632318763An Optimized Computational Framework for Isolation ForestZhen Liu0Xin Liu1Jin Ma2Hui Gao3Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaWeb Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaWeb Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaWeb Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaIsolation Forest or iForest is one of the outstanding outlier detectors proposed in recent years. Yet, in the model setting, it is mainly based on the technique of randomization and, as a result, it is not clear how to select a proper attribute and how to locate an optimized split point on a given attribute while building the isolation tree. Aiming to the two issues, we propose an improved computational framework which allows us to seek the most separable attributes and spot corresponding optimized split points effectively. According to the experimental results, the proposed model is able to achieve overall better performance in the accuracy of outlier detection compared with the original model and its related variants.http://dx.doi.org/10.1155/2018/2318763
collection DOAJ
language English
format Article
sources DOAJ
author Zhen Liu
Xin Liu
Jin Ma
Hui Gao
spellingShingle Zhen Liu
Xin Liu
Jin Ma
Hui Gao
An Optimized Computational Framework for Isolation Forest
Mathematical Problems in Engineering
author_facet Zhen Liu
Xin Liu
Jin Ma
Hui Gao
author_sort Zhen Liu
title An Optimized Computational Framework for Isolation Forest
title_short An Optimized Computational Framework for Isolation Forest
title_full An Optimized Computational Framework for Isolation Forest
title_fullStr An Optimized Computational Framework for Isolation Forest
title_full_unstemmed An Optimized Computational Framework for Isolation Forest
title_sort optimized computational framework for isolation forest
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2018-01-01
description Isolation Forest or iForest is one of the outstanding outlier detectors proposed in recent years. Yet, in the model setting, it is mainly based on the technique of randomization and, as a result, it is not clear how to select a proper attribute and how to locate an optimized split point on a given attribute while building the isolation tree. Aiming to the two issues, we propose an improved computational framework which allows us to seek the most separable attributes and spot corresponding optimized split points effectively. According to the experimental results, the proposed model is able to achieve overall better performance in the accuracy of outlier detection compared with the original model and its related variants.
url http://dx.doi.org/10.1155/2018/2318763
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