학술논문
Evaluation of Standard Classifiers for Protein Subcellular Localization
Document Type
Conference
Author
Source
2020 International Conference on Computer Science, Engineering and Applications (ICCSEA) Computer Science, Engineering and Applications (ICCSEA), 2020 International Conference on. :1-4 Mar, 2020
Subject
Language
Abstract
Finding subcellular location of protein is a well-known problem in the field of biological science. Finding out the location of protein inside the cell helps in understanding cell function, drug development, etc. This paper presents a comparison between three standard classifiers: Classification And Regression Tree (CART), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) that are used to predict protein location. The Hold-out method is used for testing the classification models. The experiment is carried out on Yeast Dataset. The results are evaluated under four metrics: Accuracy, Macro-average Precision, Macro-average Recall and Macro-F1 Score. According to results, SVM shows better Accuracy and Macro-average Precision than other two, whereas CART shows better Macro-average Recall and Macro-F1 Score.