A user driven data mining process model and learning system

Authors:
Esther Ge;Richi Nayak;Yue Xu;Yuefeng Li
Affiliations:
CRC for Construction Innovations, Faculty of Information technology, Queensland University of Technology, Brisbane, Australia;CRC for Construction Innovations, Faculty of Information technology, Queensland University of Technology, Brisbane, Australia;CRC for Construction Innovations, Faculty of Information technology, Queensland University of Technology, Brisbane, Australia;CRC for Construction Innovations, Faculty of Information technology, Queensland University of Technology, Brisbane, Australia
Venue:
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Year:
2008

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Abstract

This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A learning system framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the system. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS system and the QBFS algorithm is successfully demonstrated with a real-world application.