Methods of Assigning Labels to Detect Outliers

Shashank Kirti *

University of Lucknow, Lucknow, India.

Rajeev Pandey

University of Lucknow, Lucknow, India.

*Author to whom correspondence should be addressed.


Abstract

Outlier identification is a crucial field within data mining that focuses on identifying data points that significantly depart from other patterns in the data. Outlier identification may be categorized into formal and informal procedures. This article discusses informal approaches, sometimes known as labelling methods. The study focused on the analysis of real-time medical data to identify outliers using outlier labelling techniques. Various labelling approaches are used to calculate realistic situations in the dataset. Ultimately, using the anticipated outcomes of the outliers is a more suitable approach for addressing the needs of the larger populations.

Keywords: Outlier detection, informal methods, labeling methods, median absolute deviation


How to Cite

Kirti, S., & Pandey, R. (2024). Methods of Assigning Labels to Detect Outliers. Asian Journal of Probability and Statistics, 26(5), 42–49. https://doi.org/10.9734/ajpas/2024/v26i5617

Downloads

Download data is not yet available.

References

Hawkins DM. Identification of Outliers, Chapman & Hall, London; 1980.

Tietjen and Moore Some Grubbs-Type Statistics for the Detection of Outliers, Technometrics. 1972;14(3):583-597.

Thomas Peter Josef Linsinger, Wolfgang Kandler, Rudolf Krska, Manfred Grasserbauer The influence of different evaluation techniques on the results of interlaboratory comparisons. Springer-Verlag. 1998;3: 322–327.

Rousseeuw PJ, Croux C. Alternatives to the median absolute deviation. Journal of the American Statistical Association, Link to the paper. This paper introduces alternatives to MAD and discusses its scale dependence and the need for scaling to achieve consistency with the standard deviation. 1993; 88(424):1273-1283.

Manoj K, Senthamarai Kannan K. Comparison of methods for detecting outliers. International Journal of Scientific & Engineering Research. 2013;4(9):709–714.

Leys C, Ley C, Klein O, Bernard P, Licata L. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, Link to the paper. This source discusses the advantages of MAD over standard deviation and highlights its robustness against outliers. 2013;49(4):764-766.

Iglewicz B, Hoaglin DC. How to detect and handle outliers. The ASQC Basic References in Quality Control: Statistical Techniques, This reference manual addresses the efficiency of MAD and its application in outlier detection, including the issue of threshold selection. 1993;16:1-85.

Barbato G, Barini EM, Genta G, Levi R. Features and performance of some outlier detection methods, Journal of Applied Statistics. 2011;38:10:2133-2149.

David M, Rocke David L. Woodruff, Identification of Outliers in Multivariate Data, Journal of the American Statistical Association. 1996;91(435):1047-1061. Available:http://dx.doi.org/10.2307/2291724

Jacqueline S, Galpin and Douglas M. Hawkins Rejection of a Single Outlier in Two - or Three Way Layouts, Technometrics. 1981;23(1):65-70.

Peter J, Rousseeuw and Christophe Croux. Alternatives to the Median Absolute Deviation, Journal of the American Statistical Association. 1993;88;424:1273 - 1283.

Senthamarai Kannan K, Manoj K, Arumugam S. Labeling Methods for Identifying Outliers. International Journal of Statistics and Systems. 2015;10(2):231-238.

Senthamarai Kannan K, Manoj K. Outlier detection in multivariate data, Applied Mathematical Sciences. 2015;9(45-48):2317-2324