Basic Data Reduction Techniques and Their Influence on GAME Modeling Method

Abstract

The amount of data produced by medicine diagnosis and other means constantly increases -- in both number of measurements and in number of dimensions. For many modeling or data mining methods this increase causes problems. First main problem is well known curse of dimensionality. The second is the amount of training data items which lengthens the training process. Both these problems reduces usability of modeling methods.The aim of this article is to study several data reduction techniques and test their influence on one particular inductive modeling method -- GAME -- developed in our department. Application of each method affecting the performance (accuracy) and learning time of the GAME modeling method has been studied.To obtain representative results several datasets has been tested -- for example well known Iris dataset or real-world application for medical data (e.g. EEG classification).