Outlier Removal in Sheep Farm Datasets Using Winsorization

Hamadani, Ambreen; Ganai, Nazir Ahmad; Raja, T. A.; Alam, S.S.; Ahmad, Syed Mudasir; Hussain, Ishraq; Ahmad, Haidar Ali

doi:10.18805/bkap397

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…the upper and lower 10% of data to the 90th percentile with a little introduction of error. Hamadani et al 17 also reported similar results while using the winsorization technique. A two-sided winsorization approach was used in this study which was also reported to be better than the one-sided approach by Chambers et al 18 .…”

Section: Discussionmentioning

confidence: 65%

Evaluation and Ranking of Artificial Intelligence Algorithms for Performance Prediction in Sheep

Hamadani

Ganai

2023

Preprint

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In a rapidly transforming world, farm data is growing exponentially. Realizing the importance of this data, researchers are looking for new solutions to analyse this data and make farming predictions. Considering all this, this research was undertaken to evaluate machine learning (ML) algorithms as potential tools for evaluating 52-year data for sheep. Data was appropriately prepared was done before analysis. Winsorization was done for outlier removal. Principal component regression (PCA) and feature selection (FS) were done and based on that, three datasets were created viz. PCA, PCA+ FS, and FS bodyweight prediction. Among the 11 ML algorithms that were evaluated, the correlations between true and predicted values for MARS algorithm, Bayesian ridge regression, Ridge regression, Support Vector Machines, Gradient boosting algorithm, Random forests, XgBoost algorithm, Artificial neural networks, Classification and regression trees, Polynomial regression, K nearest neighbours and Genetic Algorithms were 0.993, 0.992, 0.991,0.991,0.991, 0.99, 0.99,0.984,0.984,0.957, 0.949.0.734 respectively for bodyweights. The top five algorithms for the prediction of bodyweights, were MARS, Bayesian ridge regression, Ridge regression, Support Vector Machines and Gradient boosting algorithm. A total of 12 machine learning models were developed for the prediction of bodyweights in sheep in the present study. It may be said that machine learning techniques can perform predictions with reasonable accuracies and can thus be viable alternatives to conventional strategies.

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Section: Discussionmentioning

confidence: 65%

Evaluation and Ranking of Artificial Intelligence Algorithms for Performance Prediction in Sheep

Hamadani

Ganai

2023

Preprint

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“…the upper and lower 10% of data to the 90th percentile with a little introduction of error. A two-sided winsorization approach was used in this study which was also reported to be better than the one-sided approach by Chambers et al 28 and Hamadani et al 29 .…”

Section: Discussionmentioning

confidence: 80%

Artificial intelligence algorithm comparison and ranking for weight prediction in sheep

Hamadani

Ganai

2023

Sci Rep

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In a rapidly transforming world, farm data is growing exponentially. Realizing the importance of this data, researchers are looking for new solutions to analyse this data and make farming predictions. Artificial Intelligence, with its capacity to handle big data is rapidly becoming popular. In addition, it can also handle non-linear, noisy data and is not limited by the conditions required for conventional data analysis. This study was therefore undertaken to compare the most popular machine learning (ML) algorithms and rank them as per their ability to make predictions on sheep farm data spanning 11 years. Data was cleaned and prepared was done before analysis. Winsorization was done for outlier removal. Principal component analysis (PCA) and feature selection (FS) were done and based on that, three datasets were created viz. PCA (wherein only PCA was used), PCA+ FS (both techniques used for dimensionality reduction), and FS (only feature selection used) bodyweight prediction. Among the 11 ML algorithms that were evaluated, the correlations between true and predicted values for MARS algorithm, Bayesian ridge regression, Ridge regression, Support Vector Machines, Gradient boosting algorithm, Random forests, XgBoost algorithm, Artificial neural networks, Classification and regression trees, Polynomial regression, K nearest neighbours and Genetic Algorithms were 0.993, 0.992, 0.991, 0.991, 0.991, 0.99, 0.99, 0.984, 0.984, 0.957, 0.949, 0.734 respectively for bodyweights. The top five algorithms for the prediction of bodyweights, were MARS, Bayesian ridge regression, Ridge regression, Support Vector Machines and Gradient boosting algorithm. A total of 12 machine learning models were developed for the prediction of bodyweights in sheep in the present study. It may be said that machine learning techniques can perform predictions with reasonable accuracies and can thus help in drawing inferences and making futuristic predictions on farms for their economic prosperity, performance improvement and subsequently food security.

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