Is it possible to make OLA as a DES?
jayahm opened this issue · 4 comments
Hi,
It is well known that OLA is a DCS method.
But, is it possible using the library to select the most competence subset of classfiers based on a certain threshold sung OLA?
Hello,
You can set selection_method='all' in order to select all base classifiers that obtained the highest competence level instead of randomly picking one among the most competent ones. However, you cannot do that based on a certain threshold.
If you want to have such functionality, you can look at the DES-P method, which selects all base classifiers according to a certain threshold, which is the performance of a random classifier (RC) in this case (1/n_classes). You can modify this code to allow the user to define a selection threshold instead of always use the RC performance.
Is there any way that I can do in such a way the top N classifiers with highest local accuracy be selected?
Can I modify DES-KNN for this purpose?
Hello,
In the current DES-KNN implementation you can set the number of classifiers (percentage) to be selected according to accuracy(pct_accuracy). However, it is expecting at least one classifier selected according to diversity (pct_diversity hyperparameter) otherwise an error is raised.
It would make sense setting pct_diversity=0 allowing the user select base models only based solely on accuracy (in this case just raising a warning to inform the user about it). I believe that change would only require a modification in the _check_parameters() method in the class to not check if the number of selected classifiers according to diversity is 0 (self.J_).
Can you work on this and send a pull request with this modification?
I see. Thank you very much.