Comparison of classifiers for different data in application of classification

Abstract

Abstract The classification task is very important in many application fields, such as image recognition, speech recognition, and text classification. Machine learning and deep learning methods are used as the classifiers in their specific classification tasks. Classical machine learning classifiers, including Random Forest, XGBoost, GMM, and SVM, and deep learning classifiers including CNN and LSTM are compared in this paper to show the different computing characteristics in their specific classification tasks. The comparison results show that the CNN-based classifier performs the best in its own classification, especially the image classification. The results illustrate that the complexity of the classification task may heavily influence the performance of the classifiers. The research in this paper has a reference significance for choosing the right classifier in applying the classification task.