Outlier Removal in Sheep Farm Datasets Using Winsorization

• Published in: Bhāratīya krṣi anusandhan patrika no. Of
• Main Authors: Hamadani, Ambreen, Ganai, Nazir A., Raja, Tariq, Alam, Safeer, Andrabi, Syed Mudasir, Hussain, Ishraq, Ahmad, Haider Ali
• Format: Journal Article
• Language: English
• Published: 26.01.2022
• ISSN: 0303-3821; 0976-4631
• DOI: 10.18805/BKAP397

Background: Sheep farm data is often biased by extreme values which are generally introduced due to errors in manual measurement. These values interfere with the accuracy of estimations especially in state-of-the-art techniques like Machine Learning. Methods: Therefore, winsorization technique was attempted for the removal of outliers from sheep farm data data for 11 years (2011-2021) for body weights at different ages. Some outliers were deliberately introduced into the data to check the efficiency of the technique. This study was conducted during the year 2021. Result: Our results indicate that outlier values of 15.3, 42, 44, 60, 90 for birth weight, weaning weight, 6-month, 9 month and 12-month body weight which were far from the normal range were removed using this technique. The mean and standard deviation values were altered after winsorization. Winsorization technique works well for sheep farm data to remove the bias introduced by outliers and also removes, to a large extent, the need for manual outlier removal in data.