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FRRI: a novel algorithm for fuzzy-rough rule induction
In this paper, we introduce a novel rule induction algorithm called Fuzzy Rough Rule Induction (FRRI).
A unified weighting framework for evaluating nearest neighbour classification
NN and FRNN perform best with a combination of Samworth rank- and distance weights and scaling by the mean absolute deviation around the median ($r_1$), the standard deviaton ($r_2$) or the interquartile range ($r_{\infty}^*$), while FNN performs best with only Samworth distance-weights and $r_1$- or $r_2$-scaling.
Evaluation of the impact of the indiscernibility relation on the fuzzy-rough nearest neighbours algorithm
In this paper, we investigate the impact of this indiscernibility relation on the performance of FRNN classification.
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