Open AccessMachine Learning Algorithm for Classification
Author(s) -
Haoyuan Tan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1994/1/012016
Subject(s) - machine learning , artificial intelligence , computer science , support vector machine , naive bayes classifier , random forest , classifier (uml) , statistical classification , field (mathematics) , structured support vector machine , task (project management) , one class classification , pattern recognition (psychology) , mathematics , engineering , systems engineering , pure mathematics
Recently, machine learning methods have a good performance in the field of classification tasks. Summarizing and comparing the performances of different classifiers in the application of their specific classification tasks has a reference significance. In this paper, five classical machine learning classifiers, including GMM, Random Forest, SVM, XGBoost, and Naive Bayes, are compared to show their computing characteristics. The advantages and disadvantages are analysed in this paper. Based on the different datasets, namely different specific classification tasks, the different classifiers perform similarly. However, the SVM-based classifier has the lowest accuracy while processing the text data to apply the text classification task. This result shows that if the classification task is difficult, the accuracy would not be high. This research summarizes the performances of different machine learning methods in the application of specific classification tasks. And this research has a reference significance for the machine learning-based classifiers.