Variable precision rough set model
Journal of Computer and System Sciences
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Graphical models for discovering knowledge
Advances in knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Transformation by Function Decomposition
IEEE Intelligent Systems
Improving Supervised Learning by Feature Decomposition
FoIKS '02 Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems
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Model development on high dimension database is very difficult. This paper presents a new rough set based machine learning method, named feature decomposition method, to discover concept hierarchies and develop a multi-hierarchy model of database. For the databases which we are familiar with, the feature group can be selected by experience of expert. When dealing with the databases without any background knowledge, a new criterion based on rough set is presented to select the features to form a feature group. According to some measures of rough set theory, the objects defined on the proposed feature group are labeled by a new intermediate concept. The concept hierarchies of the database have specific meaning, which increased the transparency of data mining process and enhance the comprehensibility of the model. Each feature group and the corresponding intermediate concept compose the structure of the database. Finally rule induction can be processed on the intermediate concepts. The algorithm presented is verified by datasets from UCI. The results show that the multi-hierarchy model established by feature decomposition method can get high classification accuracy and have better comprehensibility.