Can I drop these features to improve the accuracy of my classification problem?
If you are using a simple linear classifier, such as logistic regression then yes. That is because your plots are giving you a direct visualisation of how the model could make use of the data.
As soon as you start to use a non-linear classifier, that can combine features inside the learning model, then it is not so straightforward. Your plots cannot exclude a complex relationship that such a model might be able to exploit. Generally the only way to proceed is to train and test the model (using some form of cross-validation) with and without the feature.
A plot might visually show a strong non-linear relationship with zero linear correlation - e.g. a complete bell curve of feature versus target would have close to zero linear correlation, but suggest that something interesting is going on that would be useful in a predictive model. If you see plots like this, you can either try to turn them into linear relationships with some feature engineering, or you can treat it as evidence that you should use a non-linear model.
In general, this advice applies whether or not the features are derived features. For a linear model, a derived feature which is completely uncorrelated with the target is still not useful. A derived feature may or may not be easier for a non-linear model to learn from, you cannot easily tell from a plot designed to help you find linear relationships.