Mendeley
TY  _  JOUR
ID  -  13980502191032
TI  -  A Non-Parametric Proximity-Based Method for Outlier Detection
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Salehi Y.
AU  -  Daneshpour Negin
AD  -  دانشگاه تربیت دبیر شهید رجایی
AD  -  دانشگاه شهید رجایی
Y1  -  2019
PY  -  2019
VL  -  1
IS  -  
SP  -  16
EP  -  24
KW  -  non-parametricoutlier detectionproximity-based
DO  -  
N2  -  The detection of outliers is a task in data mining and machine learning and it’s an important step in data preprocessing. In this paper, in order to detect proximity-based outliers, a non-parametric method is proposed called NPOD. The proposed method is a combination of distance-based and density-based methods and has the ability to detect outliers in both local and global scenarios. This method does not require to determine any parameters of neighborhood radius, the threshold of existing points in the neighborhood radius, and the nearest neighbor parameters. In order to detect outliers, a new method of scoring is presented. Experimental results on the UCI datasets show that this algorithm, in spite of being non-parametric, has comparable results with previous methods. Also in some cases, it has the best performance.
UR  - rimag.ir/en/Article/28557
L1  - rimag.ir/en/Article/Download/28557
ER  -  
