一种基于粗糙集理论的训练集质量估计方法

2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2006-09-26 DOI:10.1109/SYNASC.2006.6

Y. Mota, Rafael Bello, Alberto Taboada-Crispí, A. Nowé, M. Lorenzo, G. C. Cardoso

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引用次数: 14

摘要

由于电子形式的大量数据的广泛可用性，将这些数据转化为有用知识的必要性增加了。这是一个以身作则的建议。在本文中，我们提出了评估算法用于学习分类的训练集质量的方法。我们的训练集评估依赖于粗糙集理论提供的度量。我们的实验结果涉及三种分类器(k-NN, C-4.5和MLP)应用于国际数据库。我们提出的新度量在这些测试用例上显示了良好的结果

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A New Measure Based in the Rough Set Theory to Estimate the Training Set Quality

Due to the wide availability of huge amounts of data in electronic forms, the necessity of turning such data into useful knowledge has increased. This is a proposal of learning from examples. In this paper, we propose measures to evaluate the quality of training sets used by algorithms for learning classification. Our training set assessment relies on measures provided by rough sets theory. Our experimental results involved three classifiers (k-NN, C-4.5 and MLP) applied to international data bases. The new measure we propose shows good results on these test cases

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来源期刊

2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

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