Application of Principal Component Analysis (PCA) to Reduce Multicollinearity Exchange Rate Currency of Some Countries in Asia Period 2004-2014

Alam

et al. 2022

IJAR

Background: Artificial intelligence (AI) is transforming all spheres of life and it has the potential to revolutionize animal husbandry as well. In this regard, an attempt was made to compare two AI techniques for predicting 12-month body weights of animals; viz. Principal Component regression (PCR) and Ordinary Least Squares (OLS) for datasets of Corriedale sheep spanning 11 years. Methods: PCR models were trained by varying proportions of training and testing datasets. The dataset was subject to PCR before analysis and tested (PCA dataset). A separate dataset was also created by feature selection of the PCA (PCA+FS dataset) variables. Result: The highest correlation coefficients between test and predicted variables for two datasets (PCA dataset and PCA+FS dataset) created among the multiple models trained using PCR were 0.982 and 0.9741. In terms of error, R2 or correlation coefficient, the PCA dataset performed better than the PCA+FS dataset. The second principal component had the highest explained variance in OLS (86.16%) and the highest coefficient of determination (R2) using OLS was obtained for the PCA dataset viz. 0.980. It is concluded that both the algorithms tested in this study were satisfactorily trained in their prediction of the body weights with OLS performing better than PCA in terms of R2 value.

Section: Datasetssupporting

confidence: 65%

Artificial Intelligence Techniques for the Prediction of Body Weights in Sheep

Alam

et al. 2022

IJAR

“…Our results also indicate that PCA eliminated all multicollinearity in the dataset. This has also been established in literature by several authors 9 , 10 . PCA the present study was useful in allowing for a better understanding of the correlations among the traits at the same time, ensuring that feature reduction was achieved as was also stated by 9 .…”

Section: Discussionsupporting

confidence: 75%

Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep

Ahmad

et al. 2022

Sci Rep

As the amount of data on farms grows, it is important to evaluate the potential of artificial intelligence for making farming predictions. Considering all this, this study was undertaken to evaluate various machine learning (ML) algorithms using 52-year data for sheep. Data preparation was done before analysis. Breeding values were estimated using Best Linear Unbiased Prediction. 12 ML algorithms were evaluated for their ability to predict the breeding values. The variance inflation factor for all features selected through principal component analysis (PCA) was 1. The correlation coefficients between true and predicted values for artificial neural networks, Bayesian ridge regression, classification and regression trees, gradient boosting algorithm, K nearest neighbours, multivariate adaptive regression splines (MARS) algorithm, polynomial regression, principal component regression (PCR), random forests, support vector machines, XGBoost algorithm were 0.852, 0.742, 0.869, 0.915, 0.781, 0.746, 0.742, 0.746, 0.917, 0.777, 0.915 respectively for breeding value prediction. Random forests had the highest correlation coefficients. Among the prediction equations generated using OLS, the highest coefficient of determination was 0.569. A total of 12 machine learning models were developed from the prediction of breeding values 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 for breeding value prediction.

“…ation factor, we infer that PCA indeed eliminated all multicollinearity. This has also been published by multiple authors(Pinto et al, 2006;Rahayu et al, 2017). PCA, in our study, allowed a better understanding of the correlations among the traits at the same time, ensuring that feature reduction was achieved as was also stated byPinto et al (2006).A correlation coe cient of 0.658 was reported bySolberg et al (2009) for the model to predict breeding values between true breeding values using PCR which is lower than the result of 0.746 as reported by us Du et al (2018).…”

supporting

confidence: 64%

Evaluation of Artificial Intelligence Algorithms for the Prediction of Genetic Merit

Andrabi

et al. 2022

Preprint

As the amount of data on farms grows, it is important to evaluate the potential of artificial intelligence for making farming predictions. Considering all this, this study was undertaken to evaluate various machine learning (ML) algorithms using 52-year data for sheep. Data preparation was done before analysis. Breeding values were estimated using Best Linear Unbiased Prediction. 13 ML algorithms were evaluated for their ability to predict the breeding values. The variance inflation factor for all features selected through PCA was 1. The correlation coefficients between true and predicted values for Artificial neural networks, Bayesian ridge regression, Classification and regression trees, Genetic Algorithms, Gradient boosting algorithm, K nearest neighbours, MARS algorithm, Polynomial regression, Principal component regression, Random forests, Support Vector Machines, XGBoost algorithm were 0.852, 0.742, 0.869, 0.762, 0.915, 0.781, 0.746, 0.742, 0.746, 0.917, 0.777, 0.915 respectively for breeding value prediction. Random forests had the highest correlation coefficients. A total of 13 machine learning models were developed from the prediction of breeding values 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 for breeding value prediction.