An Approach to Solve Classification Problems on Domains with Hubness Using Rough Sets and Nearest Prototype

The hubness phenomenon is an aspect of the curse of dimensionality that is related to the diminishment of specificity in similarities between points in a high-dimensional space; which is detrimental to the machine learning methods. This paper deals with evaluating the impact of hubness phenomenon on classification using the nearest prototype based on rough sets. Experimental results show that the studied methods of generation and selection of prototypes offer comparable results against others methods based on kNN approach, found in the literature, which are hubness aware and are specifically designed to deal with this problem. Based on these encouraging results and the extensibility of methods based on prototypes, it is possible to argue that it might be beneficial to use them in order to improve system performance in high dimensional data under the assumption of hubness.