Multi-class Classification Using Support Vector Machines

MATLAB 643B 200 views 0 downloads 1 credits
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Resource Overview

Input training dataset with labels and testing dataset with labels, output the classification accuracy rate on the test set

Detailed Documentation

In this task, you will input training dataset with corresponding labels and testing dataset with labels, then process them using machine learning algorithms. These algorithms will analyze and learn from the data to perform accurate classification during testing. Upon completion of classification, you will receive an accuracy rate indicating the proportion of correctly classified instances in the test set. To improve accuracy, you can experiment with different machine learning algorithms or fine-tune algorithm parameters. Additionally, you can implement data preprocessing techniques such as noise removal, feature selection, or data normalization to enhance classification performance. From an implementation perspective, you might use Scikit-learn's SVM module with One-vs-Rest or One-vs-One strategies for multi-class classification, employing functions like SVC() with appropriate kernel selection and parameter optimization through GridSearchCV.

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