Multiclass classification machine learning means categorizing data and forming groups based on similarities using different types of algorithms such as Naïve Bayes, Decision trees, SVM, Random forest classifier, KNN, and logistic regression classification.
Dear Nejood Hashim Al-walidi
Popular algorithms that can be used for multi-class classification include:
- k-Nearest Neighbors.
- Decision Trees.
- Naive Bayes.
- Random Forest.
- Gradient Boosting.
I suggest Naive Bayes and k-Nearest Neighbors algorithms.
Hope this helps. Stay Home Stay Safe.
Regards
The methods/Algorithms you mentioned are all popular well-established for many kind of data bases. In particular, no single method is optimal for every data base. Recent trend is however., on nonlinear method with comparatively more accuracy.
Every algorithm may have pros and cons, but we may need to select appropriate model for the given data at hand. Here, I would like to provide a link for SVM with MATLAB code : https://au.mathworks.com/matlabcentral/fileexchange/62061-multi-class-svm
Hi,
Depends on dataset , choose classifier. Probably, Random forest is my choice to multiclass classification. For feature selection PSO algorithm helps you.
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