A new unsupervised outlier detection method

Article type: Research Article

Authors: Zheng, Lina^a | Chen, Lijun^{b; *} | Wang, Yini^{c; *}

Affiliations: [a] School of Economics, Guangxi University, Nanning, Guangxi, P.R. China | [b] Center for Applied Mathematics of Guangxi, Yulin Normal University, Yulin, Guangxi, P.R. China | [c] Guangxi Key Laboratory of Seaward Economic Intelligent System Analysis and Decision, Guangxi University of Finance and Economics, Nanning, Guangxi, P.R. China

Correspondence: [*] Corresponding author. Lijun Chen. E-mail: [email protected] and Yini Wang. E-mail: [email protected].

Abstract: Information amount has been shown to be one of the most efficient methods for measuring uncertainty. However, there has been little research on outlier detection using information amount. To fill this void, this paper provides a new unsupervised outlier detection method based on the amount of information. First, the information amount in a given information system is determined, which offers a thorough estimate of the uncertainty of this information system. Then, the relative information amount and the relative cardinality are proposed. Following that, the degree of outlierness and weight function are shown. Furthermore, the information amount-based outlier factor is constructed, which determines whether an object is an outlier by its rank. Finally, a new unsupervised outlier detection method called the information amount-based outlier factor (IAOF) is developed. To validate the effectiveness and advantages of IAOF, it is compared to five existing outlier identification methods. The experimental results on real-world data sets show that this method is capable of addressing the problem of outlier detection in categorical information systems.

Keywords: Outlier detection, CIS, Information amount, IAOF

DOI: 10.3233/JIFS-236518

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1713-1734, 2024