FINkNN: A Fuzzy Interval Number k-Nearest Neighbor Classifier for Prediction of Sugar Production from Populations of Samples

摘要

This work introduces FINkNN, a k-nearest-neighbor classifier operating over the metric lattice ofconventional interval-supported convex fuzzy sets. We show that for problems involvingpopulations of measurements, data can be represented by fuzzy interval numbers (FINs) and wepresent an algorithm for constructing FINs from such populations. We then present a lattice-theoreticmetric distance between FINs with arbitrary-shapedmembership functions, which formsthe basis for FINkNN's similarity measurements. We apply FINkNN to the task of predictingannual sugar production based on populations of measurements supplied by Hellenic SugarIndustry. We show that FINkNN improves prediction accuracy on this task, and discuss thebroader scope and potential utility of these techniques. color="gray">