I am working on a classification problem, where I have 400 features(all are numeric), and 100 classes and I have 26,000 examples for training. In my project I am using Weka and I have tried different algorithms and so far I have received the best results with the following stack of algorithms:
- Rotation Forest(with 60 Iteration)+Multiclass classifier(1-vs-all)+RepTree-> accuracy 79.9%
- Bagging(with 60 Iteration)+Multiclass classifier(1-vs-all)+RepTree->accuracy is 79%
I have tried also to increase the number of Iterations but the accuracy decreases after 60 Iterations
Q: Is it possible to increase the accuracy, and which algorithm or methodology can be used to achieve better accuracy?