On the Accuracy of Meta-learning for Scalable Data Mining
Journal of Intelligent Information Systems
Identifying Relevant Databases for Multidatabase Mining
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Advanced Dynamic Selection of Diagnostic Methods
CBMS '98 Proceedings of the Eleventh IEEE Symposium on Computer-Based Medical Systems
An extensible meta-learning approach for scalable and accurate inductive learning
An extensible meta-learning approach for scalable and accurate inductive learning
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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The results of knowledge discovery in data bases could vary depending on the data mining method. There are several ways to select the most appropriate data mining method dynamically. One proposed method clusters the whole domain area into "competence areas" of the methods. A metamethod is then used to decide which data mining method should be used with each data base instance. However, when knowledge is extracted from several data bases knowledge discovery may produce conflicting results even if the separate data bases are consistent. At least two types of conflicts may arise. The first type is created by data inconsistency within the area of the intersection of the data bases. The second type of conflicts is created when the metamethod selects different data mining methods with inconsistent competence maps for the objects of the intersected part. We analyze these two types of conflicts and their combinations and suggest ways to handle them.