Fuzzy Set-Based Isolation Forest

被引：22

作者：

Karczmarek, Pawel ^{[1
]}

Kiersztyn, Adam ^{[1
]}

Pedrycz, Witold ^{[2
,3
,4
]}

机构：

[1] Lublin Univ Technol, Dept Comp Sci, Lublin, Poland

[2] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada

[3] King Abdulaziz Univ, Dept Elect & Comp Engn, Jeddah, Saudi Arabia

[4] Polish Acad Sci, Syst Res Inst, Warsaw, Poland

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | 2020年

关键词：

isolation forest; membership value; fuzzy set-based isolation forest; outlier detection; anomaly score; ANOMALY DETECTION; ALGORITHMS; OUTLIERS;

D O I：

10.1109/fuzz48607.2020.9177718

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

One of the main challenges is the analysis of large data sets, in particular those containing various types of data, such as time, place, image, and those assuming categorical values. This type of data may contain numerous outliers. Despite the continuous development of data analysis, many methods can be effectively improved, in particular through the use of efficient solutions based on fuzzy set technologies. In this paper, we analyze the improvement of a well-known method, i.e. Isolation Forest, for which we introduce an innovative modification, referred to as the Fuzzy Set-Based Isolation Forest.

引用

页数：6

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