LVQ algorithm with instance weighting for generation of prototype-based rules

Crisp and fuzzy-logic rules are used for comprehensible representation of data, but rules based on similarity to prototypes are equally useful and much less known. Similarity-based methods belong to the most accurate data mining approaches. A large group of such methods is based on instance selection and optimization, with the Learning Vector Quantization (LVQ) algorithm being a prominent example. Accuracy of LVQ depends highly on proper initialization of prototypes and the optimization mechanism. This paper introduces prototype initialization based on context dependent clustering and modification of the LVQ cost function that utilizes additional information about class-dependent distribution of training vectors. This approach is illustrated on several benchmark datasets, finding simple and accurate models of data in the form of prototype-based rules.

► Understanding data structures using prototype-based rules requires selection and optimization of prototypes.
► New version of the LVQ algorithm, based on instance weighting, has been proposed and two different weighting strategies applied.
► Numerical experiments on several datasets have been performed and results compared with the state-of-the-art LVQ1 model.
► Weighted LVQ algorithm may find accurate and comprehensible representation of data.