Ywz2023 / classification_svm

Multi-Class and Multi-Label Classification using Support Vector Machines

classification_svm

Multi-Class and Multi-Label Classification using Support Vector Machines

Overview

Multi-Class and Multi-Label Classification Using Support Vector Machines on Anuran Cells Data Set. The dataset contains three labels and each label has multiple classes.

Outcomes

• Trained SVM for each labels using Gaussian Kernels and one versus all classifiers. Also, trained the model using L1-penalized SVMs.
• Used 10 fold CV to determine the weight of SVM penalty and width of the Gaussian Kernel.
• Used SMOTE to remedy class imbalance and trained the classifiers.

Stack

• Ski-kit learn
• Pandas Library
• Python

Dataset

Dataset from (https://archive.ics.uci.edu/ml/datasets/Anuran+Calls+%28MFCCs)