Analyzing different prototype selection techniques for dynamic classifier and ensemble selection
This work addresses performance and efficiency issues in dynamic classifier and ensemble selection for machine learning practitioners, but it is incremental as it builds on existing techniques.
The paper tackled the sensitivity of dynamic selection techniques to validation set distribution by evaluating six prototype selection methods to edit validation data, resulting in improved classification accuracy and significantly reduced computational cost across 30 classification problems.
In dynamic selection (DS) techniques, only the most competent classifiers, for the classification of a specific test sample are selected to predict the sample's class labels. The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample. The classifiers' competence is usually estimated using the neighborhood of the test sample defined on the validation samples, called the region of competence. Thus, the performance of DS techniques is sensitive to the distribution of the validation set. In this paper, we evaluate six prototype selection techniques that work by editing the validation data in order to remove noise and redundant instances. Experiments conducted using several state-of-the-art DS techniques over 30 classification problems demonstrate that by using prototype selection techniques we can improve the classification accuracy of DS techniques and also significantly reduce the computational cost involved.