While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines:
1) If accuracy is a concern, test different algorithms and cross-validate them
2) If the training dataset is small, use models that have low variance and high bias
3) If the training dataset is large, use models that have high variance and little bias