Evaluation of the impact of the indiscernibility relation on the fuzzy-rough nearest neighbours algorithm
This work addresses the impact of similarity measures on FRNN for classification tasks, but it is incremental as it builds on existing methods with specific improvements.
The paper investigates how different indiscernibility relations affect the performance of the fuzzy-rough nearest neighbours (FRNN) classification algorithm, finding that the Neighbourhood Components Analysis algorithm performs best by trading speed for accuracy.
Fuzzy rough sets are well-suited for working with vague, imprecise or uncertain information and have been succesfully applied in real-world classification problems. One of the prominent representatives of this theory is fuzzy-rough nearest neighbours (FRNN), a classification algorithm based on the classical k-nearest neighbours algorithm. The crux of FRNN is the indiscernibility relation, which measures how similar two elements in the data set of interest are. In this paper, we investigate the impact of this indiscernibility relation on the performance of FRNN classification. In addition to relations based on distance functions and kernels, we also explore the effect of distance metric learning on FRNN for the first time. Furthermore, we also introduce an asymmetric, class-specific relation based on the Mahalanobis distance which uses the correlation within each class, and which shows a significant improvement over the regular Mahalanobis distance, but is still beaten by the Manhattan distance. Overall, the Neighbourhood Components Analysis algorithm is found to be the best performer, trading speed for accuracy.