"An outliers detection and elimination framework in classification task" by Ch Sanjeev Kumar Dash, Ajit Kumar Behera et al.
 

An outliers detection and elimination framework in classification task of data mining

Article Type

Research Article

Publication Title

Decision Analytics Journal

Abstract

An outlier is a datum that is far from other data points in which it occurs. It can have a considerable impact on the output. Therefore, removing or resolving it before the analysis is essential to prevent skewing. Outliers in a survey sampling can have a significant outcome on statistical results. The goal of discovering outliers in data mining is to find a pattern in data that does not conform to expected behavior. In this paper, we have proposed a framework in which a popular statistical approach termed Inter-Quartile Range (IQR) is used to detect outliers in data and deal with them by Winsorizing method. A radial basis function network trained by teaching a learning-based optimization model is developed using the preprocessed dataset under this framework. A few standard University of California Irvine (UCI)datasets are employed to measure the framework's effectiveness. The outcome of the experiments shows that our proposed framework can be a viable alternative tool for the classification task of data mining where prior outliers preprocessing is necessary.

DOI

https://10.1016/j.dajour.2023.100164

Publication Date

3-1-2023

Comments

Open Access, Gold

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