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 of conventional interval-supported convex fuzzy sets. We show that for problems involving populations of measurements, data can be represented by fuzzy interval numbers (FINs) and we present an algorithm for constructing FINs from such populations. We then present a lattice-theoretic metric distance between FINs with arbitrary-shaped membership functions, which forms the basis for FINkNN's similarity measurements. We apply FINkNN to the task of predicting annual sugar production based on populations of measurements supplied by Hellenic Sugar Industry. We show that FINkNN improves prediction accuracy on this task, and discuss the broader scope and potential utility of these techniques.