【期刊论文】常见数据分布类型中不同异常值检测方法的有效性研究

SOONG, CHINGTSING

April 27, 2024

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1808 words

科学研究

> 于2024年4月27日，在期刊Journal of Computer Technology and Applied Mathematics (JCTAM)正式发表。

##Title

Research on the Effectiveness of Different Outlier Detection Methods in Common Data Distribution Types

## Author

Qingqing Song
Belarusian State University

Shaoliang Xia
Belarusian State University

## Abstract

Outlier detection are widely applied in areas such as network performance optimization and pre-processing of machine learning data. In the field of machine learning, the objective is to enhance data quality, thereby improving the performance of subsequent statistical analyses or machine learning models. Currently, there are numerous effective and reliable outlier analysis methods, and their effectiveness varies significantly when dealing with different types of data distributions. Therefore, it is essential to select an appropriate outlier analysis method. In this study, we conducted outlier detection on sample data from five continuous probability distributions (including Normal, Chi-square, Exponential, Gamma, and T distributions) and four discrete probability distributions (including Binomial, Poisson, Geometric, and Hypergeometric distributions). This paper employs five outlier detection methods, namely Z-Score, IQR, DBScan, Isolation Forest, and Random Forest, and evaluates the detection effectiveness of these methods. Through comparison and analysis, this paper summarizes the characteristics of various outlier detection methods when dealing with sample data from different types of distributions. These findings will assist us in making more rational method selections when facing different outlier detection scenarios.

## Keywords

Outlier Detection, Outlier Analysis, Machine Learning, Data Distribution, Performance Evaluation, Data Preprocessing

【期刊论文】常见数据分布类型中不同异常值检测方法的有效性研究

SOONG, CHINGTSING • 2024 年 04 月 27 日

【期刊论文】常见数据分布类型中不同异常值检测方法的有效性研究

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【期刊论文】常见数据分布类型中不同异常值检测方法的有效性研究

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【期刊论文】常见数据分布类型中不同异常值检测方法的有效性研究

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