Towards Identifying Multicriteria Outliers

2018 ◽  
Vol 10 (3) ◽  
pp. 27-38
Author(s):  
Baroudi Rouba ◽  
Safia Nait-Bahloul
Keyword(s):  
Outlier Detection ◽  
Decision Aid ◽  
Detection Method ◽  
Real Life ◽  
Country Risk ◽  
Multicriteria Decision Aid ◽  
Financial Problem ◽  
Outranking Method ◽  
Outranking Relations ◽  
Local Outlier

This article tackles the problem of outlier detection in the multicriteria decision aid (MCDA) field. The authors propose an outlier detection method based on binary outranking relations and Local Outlier Factor (LOF) algorithm. The outlier is detected by applying LOF algorithm on the distribution of the outranking relations generated by a multicriteria outranking method. The proposed approach is illustrated on an artificial example and evaluated on a real life financial problem, the country risk problem.

scholarly journals Outlier Detection Method for Flash Flood Disaster Monitoring Data based on Information Entropy

2021 ◽  
Vol 2138 (1) ◽  
pp. 012013
Author(s):  
Yongzhi Chen ◽  
Ziao Xu ◽  
Chaoqun Niu
Keyword(s):  
Outlier Detection ◽  
Information Entropy ◽  
Detection Method ◽  
Flash Flood ◽  
False Positive Rate ◽  
Flood Disaster ◽  
Detection Methods ◽  
Positive Rate ◽  
Disaster Monitoring ◽  
Local Outlier

Abstract In the research of flash flood disaster monitoring and early warning, the Internet of Things is widely used in real-time information collection. There are abnormal situations such as noise, repetition and errors in a large amount of data collected by sensors, which will lead to false alarm, lower prediction accuracy and other problems. Aiming at the characteristic that outliers flow of sensors will cause obvious fluctuation of information entropy, this paper proposes a local outlier detection method based on information entropy and optimized by sliding window and LOF (Local Outlier Factor). This method can be used to improve the data quality, thus improving the accuracy of disaster prediction. The method is applied to data stream processing of water sensor, and the experimental results show that the method can accurately detect outliers. Compared with the existing detection methods that only use data distance to determine, the test positive rate is improved and the false positive rate is reduced.

A net-flow based approach to detect outliers in multicriteria decision problems

2021 ◽  
pp. 1-12
Author(s):  
Baroudi Rouba
Keyword(s):  
Decision Aid ◽  
Statistical Approach ◽  
Real Life ◽  
Research Direction ◽  
Decision Problems ◽  
Multicriteria Decision Aid ◽  
Multicriteria Decision ◽  
Promethee Method ◽  
Multicriteria Decision Problems ◽  
New Research

Detecting outliers in multicriteria decision aid (MCDA) field is a new research direction that has not been enough explored. This paper tackles this problem by proposing a statistical approach based on PROMETHEE method net-flow. To consider the multicriteria character of the problem, the net-flow of each object is computed by applying PROMETHEE method. According to the normal distribution of the net-flow values, the standard deviation (SD) or interquartile range (IQR) statistical methods are used to detect outliers. To prove its applicability, the proposed approach is evaluated on real life problem.

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