I have training data set with around 1500 positive set samples and 4500 negative set samples. All the features are numeric( floating or integer type values) and the data is specific to bio-informatics domain. I have tried using SVM and random forest, both give me an accuracy of around 88% on test set of size 700 and containing only positive samples. I have also tried LDA and adaboost in R, but they give worse results. I have tried feature selection which I observed improves the prediction accuracy by another 1.5%. Algorithm used for feature selection was random forest. I have two questions:
- I need to improve my classification accuracy on the given test set.Please guide me how I can achieve the same.
- Also, if there has to some step wise strategy that goes into selecting a classification algorithm. Thanks