Haematococcus pluvialis cell based on support vector machine Classification research

Abstract

The classification of Haematococcus pluvialis cells, a support vector machine was used to construct a model, and 150 groups of Haematococcus pluvialis data were used to fit the model. The data set and test set of Haematococcus pluvialis were used separately. For classification prediction, different kernel functions are selected to compare the classification accuracy of support vector machines, and learning curve are used to adjust the parameters of part of the eucalyptus function. Finally, the model measurement standards of confusion moment and ROC curve are introduced. It is concluded that the support vector machine classifier can classify Haematococcus pluvialis in the proliferation and induction stages, and the classification accuracy under the multi-directional kernel function and sigmoid is the highest, with a classification accuracy of 91.11%.