Open AccessBinary Priority Outlier Classifier Based Outlier Elimination
Author(s) -
Deoras Tejas Tushar et.al
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i3.1717
Subject(s) - outlier , computer science , classifier (uml) , data mining , anomaly detection , binary classification , artificial intelligence , binary number , pattern recognition (psychology) , mathematics , support vector machine , arithmetic
Outliers are records that deviate from normal behavioral pattern. This causes a serious issue when it comes to analysing data. In the recent years there has been great research to identify these outliers. Identifying them not only helps improve analysis of data but also provides many applications. The paper presents a way of indenting these outliers based on priority assigned to the attributes. The priorities are then added for each record in the dataset and the pattern is analysed. A concept based on interquartile range is used to eliminate the outliers. Hence the classifier divides the dataset into two classes: outliers and normal data.