Pooja Pun, Avdesh Mishra, Simon Lailvaux, Md Tamjidul Hoque A Machine Learning Approach to Functional Morphology and Performance Prediction Pooja Pun, Avdesh Mishra, Simon Lailvaux, Md Tamjidul Hoque Email: ppun@uno.edu, amishra2@uno.edu, slailvau@uno.edu, thoque@uno.edu Department of Computer Science, Department of Biological Sciences, University of New Orleans, New Orleans, LA, USA Introduction Functional morphology involves the study of the relationship between the physical structures and the functions of the various parts of an organism. Morphology-to-performance relationships are well explained for individual performance traits, particularly in species that are highly specialized for conducting a specific biological task. But the same relationship is ambiguous and controversial for extinct species, mainly because these species have no living analogues, which makes it difficult to validate the predicted relationship obtained by the extrapolation from the form and function relationships of other living animals, to which they bear little resemblance. Classification Table 1: Accuracy and MCC for classification Feature selection before base layers Overall Accuracy Matthews Correlation Coefficient Support Vector Machines (SVM) 0.94537 0.93264 Stacking without feature selection 0.93191 0.916 SVM after feature selection 0.94695 0.93455 Stacking after feature selection 0.94062 0.92672 Stacking with feature selection before meta layer 0.94141 0.92773 Base Layers: SVC, LogReg, XGBC Feature Selection before meta layer Meta Layer: SVC Fig 1: Flowchart for classification Regression The dataset was divided into ten test and train datasets which were used to train and test different regressors. The best performing regressors were combined in a stacking manner for more robust results. Objectives Proper understanding of the morphology-to-performance relationships of extinct animals, mainly Australian Marsupials Rigorously validate the predictions made by using machine learning tools Results Methods Training Datasets Dataset containing functional and morphological data of 31 lizard species with a total of 1263 samples was provided by the Department of Biological Sciences. Missing values in the dataset were replaced by two-step k-Nearest Neighbor (kNN) based approach. In the first step, the missing value of a target sample, belonging to a target species, was replaced by the average value of five samples closest to the target sample determined by computing the euclidean distance between the target sample and all other samples present in the target species. In the second step, the same process was applied but to the whole dataset. Machine Learning Methods Feature Selection is done using an evolutionary algorithm.. In stacking framework, classifiers are stacked in two layers: base layer and meta layer. The results from the base layer is a basis for the dataset feed into the meta layer. Fig 3: PCC and MAE for different regression methods for Bite power Table 2: PCC and MAE for regression of Bite power Pearson Correlation Coefficient (PCC) Mean Absolute Error (MAE) Gradient Boosting Regressor (without using Test and Train Datasets) 0.73044 0.85863 XGBoost Regressor (using Test and Train Datasets) 0.95016 1.10059 Stacking 0.97637 0.72926 Fig 2: Accuracy and MCC for Classification Discussions kNN played a key role in obtaining high accuracy in both classification and regression. Stacking with feature selection elevated the accuracy for both classification and regression. Conclusions Our results suggest that machine learning is a promising approach for making accurate predictions of performance abilities from morphology alone. Future applications of these methods to the morphology of extinct organisms may allow us to reduce the uncertainty in performance prediction whilst making fewer assumptions. Acknowledgements We gratefully acknowledge the Louisiana Board of Regents through the Board of Regents Support Fund, LEQSF (2016-19)-RD-B-07. We also gratefully acknowledge the University of New Orleans for the Internal FY19 IGD Fund, Award #: CON000000002946.