SVM Multi-Class Classification Using One-vs-One Strategy

This method selects samples from one class as positive examples while using samples from only one other class as negative examples for each binary classifier (known as "One-vs-One" approach). This prevents dataset skewness. The process involves creating multiple classifiers - the first determines "Class 1 vs Class 2", the second "Class 1 vs Class 3", and so on. For k classes, the total number of binary classifiers required is k(k-1)/2 (e.g., 10 classifiers for 4 classes). Although more classifiers are needed, the total training time is significantly reduced compared to the "One-vs-Rest" method.