Training dataset reduction algorithms
An important drawback of instance-based learning schemes is the necessity of comparing new arriving instances whose labels are meant to be predicted to all already stored ones (“training” data). In order to do so, all instances have to be iterated which leads to potentially—depending on the amount of data—high computational effort in the prediction phase.Typically, two main approaches to improve the performance of nearest neighbour classifiers are pointed out [In terms of horizontal thinning, the concept of feature selection has been applied in the context of pattern-recognition-based prosthetic control for large feature set dimensions, for instance biologically inspired methods such as genetic algorithms and particle swarm optimization [However, the examinations made in this work cope with vertical data reduction techniques. The general idea is to reduce the computational effort of prediction steps in instance-based learning by decreasing the number of instances within the training set. This process is usually referred to as instance reduction or prototype reduction [Prototype reduction methods can be divided into prototype selection (vertical thinning) on the one hand [