I am trying to classify an indicator of health as 0 and 1. I have an imbalanced dataset (0 : 5700, 1:1700) where all the values are binary (0 and 1 only for all features and target). I applied many resampling algorithms (random resampling, combination of SMOTEN (since only categorical values here: binary features only; total 200 features) and random under sample, resampling by genetic program etc). But couldn't get any improvement (while looking performance by analysing ROC curve, model without resampling performs slightly better than model with resampling).
If all the values are binary (predictors and target) in imbalanced classification problem, could you please suggest a good resampling technique other than random resampling, GP and SMOTEN?