improving the svm result

how i can improve the svm results, for large data set

Answer 1

[Hari]

Hi Rishika,

I understand that you want to improve the results of Support Vector Machine algorithm (SVM algorithm) for the large data sets.

To improve the results of SVM for a large dataset, you can consider the following strategies:

1. Feature Selection/Extraction: Reduce the dimensionality of your dataset by selecting or extracting the most relevant features. This can help to focus on the most informative aspects of your data and improve the SVM's performance.
2. Sampling Techniques: If your dataset is imbalanced, consider using sampling techniques such as oversampling the minority class, under-sampling the majority class, or using more advanced techniques like SMOTE (Synthetic Minority Over-sampling Technique). This can help to balance the class distribution and improve the SVM's performance on the minority class.
3. Parameter Tuning: Optimize the hyperparameters of the SVM algorithm. Grid search or random search can be used to find the best combination of hyperparameters such as the regularization parameter (C) and the kernel parameter (gamma). Cross-validation can be employed to evaluate different parameter settings and select the best one.
4. Parallelization: Utilize parallel computing techniques to speed up the training process, especially for large datasets. MATLAB provides options for parallel computing, such as the Parallel Computing Toolbox, which can help to distribute the workload across multiple processors or cores.
5. Data Subset: If training time is a concern, consider using a subset of your data for training. You can randomly sample a smaller portion of your dataset while ensuring it maintains the same class distribution. This can help to reduce training time while still providing reasonable results.
6. Ensemble Methods: Explore ensemble methods such as bagging or boosting to improve the performance of the SVM. By combining multiple SVM models, you can potentially reduce overfitting and improve generalization.

Refer to the below documentation to learn more about “Feature Extraction”.

https://www.mathworks.com/discovery/feature-extraction.html

Refer to the below article to learn more about SMOTE technique.

https://www.mathworks.com/matlabcentral/fileexchange/75401-synthetic-minority-over-sampling-technique-smote

Refer to the below article “Train Classifier Using Hyperparameter Optimization in Classification Learner App” to learn more about Parameter tunning.

https://www.mathworks.com/help/stats/train-classifier-using-hyperparameter-optimization-in-classification-learner-app.html

Refer to the below documentation to learn more about Parallelization techniques in MATLAB.

https://www.mathworks.com/products/parallel-computing.html