Performance Analysis of Machine Learning Methods

Abstract

Abstract Machine Learning has been studied worldwide for its functions in data science and artificial intelligence (AI) fields. Previous works have shown the excellent performance of machine learning methods in image classification. This paper uses various machine learning methods for fashion product classification. This paper aims to analyze the result of predictions for all classes and the first three ranked classes, and meanwhile, compare and discuss Support Vector Machine (SVM), K Nearest Neighbor (KNN), Convolution Neural Network (CNN), Contrastive Language-Image Pre-training (CLIP) methods’ performance. The results show that the F-Score is increased if just predicted for the first three ranked classes, and among SVM, KNN, and CNN models, CNN is the best in both conditions. From the performance of all four models, CLIP was the best model with better learning ability. Besides, the results suggest that an imbalanced dataset may harm predictions, and the CLIP method yields the best result. In the future, CLIP methods may be more likely recommended in an image classification problem with lots of classes, and an imbalanced dataset adjusted will provide new insights into unsolved and unimproved classification problems.