RELIEF is a feature selection algorithm used in binary classification (generalisable to polynomial classification by decomposition into a number of binary problems) proposed by Kira and Rendell in 1992. Its strengths are that it is not dependent on heuristics, runs in low-order polynomial time, and is noise-tolerant and robust to feature interactions, as well as being applicable for binary or continuous data; however, it does not discriminate between redundant features, and low numbers of training instances fool the algorithm. Kononenko et al. proposed some updates to the algorithm (RELIEFF) in order to improve the reliability of the probability approximation, make it robust to incomplete data, and generalising it to multi-class problems.