NDLI: MaxMin: An Unbiased Outlier Detection Method

Content Provider	IEEE Xplore Digital Library
Author	Yan Li-rong Wu Yi-bo
Copyright Year	2015
Description	Author affiliation: Dept. of Inf., Wuhan Gen. Hosp. of Guangzhou Command, Wuhan, China (Yan Li-rong) \|\| Dept. of Unmanned Aerial Vehicle, Wuhan Mech. Technol. Coll., Wuhan, China (Wu Yi-bo)
Abstract	Purpose To study the outlier detection methods and supply practical tools for data mining. Methods A novel parametric outlier detection method called maximum minimum (MaxMin) was presented to solve the crucial outlier detection issue, i.e., the estimation of the distribution parameters of the target normal distribution 'contaminated' with the outliers. Through an iterative rejection sampling procedure, a normal parametric probability density function was obtained to maximize the minimum ratio of nonparametric probability density to parametric probability density which is calculated based on each single sample. Simulation datasets, consisting of 2000 samples following the standard normal distribution contaminated by different portions of outlier following a Weibull distribution, were analysed to evaluate the performance of the MaxMin method. Results The simulation revealed that the distribution parameters could be precisely and stably estimated regardless of the proportion of the outliers in the whole dataset. The MaxMin procedure was mathematically demonstrated to be able to obtain the unbiased estimation of the distribution parameters when the outliers were not in the kernel region of the target distribution (e.g. [μ - σ, μ + σ]). Conclusion The unbiased estimation of the target distribution can be obtained by using MaxMin method under a relatively weak assumption. The outlier detection method has high precision and robustness.
Sponsorship	Tongji Univ.
Starting Page	297
Ending Page	303
File Size	506708
Page Count	7
File Format	PDF
e-ISBN	9781467368506
DOI	10.1109/ICISCE.2015.73
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parametric method Parameter estimation Unbiased estimation Sociology Estimation Gaussian distribution Probability density function Outlier detection MaxMin Standards
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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