Effect of polynomial, radial basis, and Pearson VII function kernels in support vector machine algorithm for classification of crayfish

Freshwater crayfish are one of the most important aquatic organisms that play a pivotal role in the aquatic food chain as well as serving as bioindicators for the aquatic ecosystem health assessment. Hemocytes, the blood cells of crustaceans, can be considered stress and health indicators in crayfish, and are used to evaluate the health response. Therefore, total hemocyte cell numbers (THCs) are useful parameters to show the health of crustaceans and serve as stress indicators to decide the quality of the habitat. Since, catching the fish and the other aquatic organisms, and collecting the data for further assessments are time-consuming and frustrating, today, scientists tend to use swift, more sophisticated, and more reliable methods for modeling the ecosystem stressors based on bioindicators. One tool which has attracted the attention of science communities in the last decades is machine learning algorithms that are reliable and accurate methods to solve classification and regression problems. In this study, a support vector machine is carried out as a machine learning algorithm to classify healthy and unhealthy crayfish based on physiological characteristics. To solve the non-linearity problem of the data by transporting data to high-dimensional space, different kernel functions including polynomial (PK), Pearson VII function-based universal (PUK), and radial basis function (RBF) kernels are used and their effect on the performance of the SVM model was evaluated. Both PK and PUK functions performed well in classifying the crayfish. RBF, however, had an adverse impact on the performance of the model. PUK kernel exhibited an outstanding performance (Accu-racy = 100%) for the classification of the healthy and unhealthy crayfish.

Automated summary

In this study, a support vector machine algorithm was used to classify healthy and unhealthy crayfish based on physiological characteristics. Different kernel functions had varying effects on the performance of the model, with the Pearson VII function-based universal kernel exhibiting outstanding performance.