Research on bioinformatics data classification method based on support vector machine

Due to the problems of low classification accuracy and long classification time in traditional biological information data classification methods, a biological information data classification method based on support vector machine is proposed. Bio-information data was acquired through gene expression and the characteristics analysed. Based on the data analysis results, outlier detection and data scaling for the acquired bio-information data are carried out. Based on the processing results, mutual information is used to measure the correlation and redundancy, then, the bio-information data features are selected through the feature selection algorithm of minimum redundancy and maximum correlation, and finally, the selected bio-information data features are taken as data samples. Through support vector machine, the classification decision function is established under the conditions of linear and non-separable data samples to obtain the classification results of biological information data. The experimental results show that the proposed method has higher classification accuracy and shorter classification time.