Basic Data Reduction Techniques and Their Influence on GAME Modeling Method

Miroslav Cepek, M. Snorek
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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).
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基本数据约简技术及其对博弈建模方法的影响
医学诊断和其他手段产生的数据量不断增加——在测量数量和维度数量上都是如此。对于许多建模或数据挖掘方法,这种增加会导致问题。第一个主要问题是众所周知的维度诅咒。第二是训练数据项的数量,这延长了训练过程。这两个问题都降低了建模方法的可用性。本文的目的是研究几种数据约简技术,并测试它们对我们部门开发的一种特定的归纳建模方法——GAME的影响。研究了每种方法的应用对GAME建模方法的性能(精度)和学习时间的影响。为了获得具有代表性的结果,已经测试了几个数据集——例如众所周知的虹膜数据集或医疗数据的实际应用(例如脑电图分类)。
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