A Rough Entropy-Based Weighted Density Outlier Detection Method for Two Universal Sets

2018 ◽  
pp. 509-516 ◽  
Author(s):  
T. Sangeetha ◽  
Amalanathan Geetha Mary
Keyword(s):  
Outlier Detection ◽  
Detection Method ◽  
Weighted Density

A new outlier detection method based on convex optimization: application to diagnosis of Parkinson’s disease

2020 ◽  
pp. 1-20
Author(s):  
Pakize Taylan ◽  
Fatma Yerlikaya-Özkurt ◽  
Burcu Bilgiç Uçak ◽  
Gerhard-Wilhelm Weber
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convex Optimization ◽  
Outlier Detection ◽  
Detection Method

A new outlier detection method based on OPTICS

2019 ◽  
Vol 45 ◽  
pp. 197-212 ◽  
Author(s):  
Yue Fei Wang ◽  
Yu Jiong ◽  
Guo Ping Su ◽  
Yu Rong Qian
Keyword(s):  
Outlier Detection ◽  
Detection Method

Outlier Detection for Soft-Sensor Modeling Data Based on k-Nearest Neighbor

2012 ◽  
Vol 468-471 ◽  
pp. 2504-2509
Author(s):  
Qiang Da Yang ◽  
Zhen Quan Liu
Keyword(s):  
Outlier Detection ◽  
Nearest Neighbor ◽  
Detection Method ◽  
Soft Sensor ◽  
K Nearest Neighbor ◽  
Sensor Model ◽  
Sensor Technique ◽  
On Line ◽  
Modeling Data ◽  
Sensor Modeling

The on-line estimation of some key hard-to-measure process variables by using soft-sensor technique has received extensive concern in industrial production process. The precision of on-line estimation is closely related to the accuracy of soft-sensor model, while the accuracy of soft-sensor model depends strongly on the accuracy of modeling data. Aiming at the special character of the definition for outliers in soft-sensor modeling process, an outlier detection method based on k-nearest neighbor (k-NN) is proposed in this paper. The proposed method can be realized conveniently from data without priori knowledge and assumption of the process. The simulation result and practical application show that the proposed outlier detection method based on k-NN has good detection effect and high application value.

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