An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition

Authors:
Geoffrey I. Webb;Jason Wells;Zijian Zheng
Affiliations:
School of Computing and Mathematics, Deakin University, Geelong, Victoria 3217, Australia. webb@deakin.edu.au;School of Computing and Mathematics, Deakin University, Geelong, Victoria 3217, Australia. wells@deakin.edu.au;School of Computing and Mathematics, Deakin University, Geelong, Victoria 3217, Australia. zijian@deakin.edu.au
Venue:
Machine Learning
Year:
1999

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Abstract

Machine learning and knowledge acquisition from expertshave distinct capabilities that appear to complement one another. Wereport a study that demonstrates the integration of these approachescan both improve the accuracy of the developed knowledge base andreduce development time. In addition, we found that users expectedthe expert systems created through the integrated approach to havehigher accuracy than those created without machine learning and ratedthe integrated approach less difficult to use. They also providedfavorable evaluations of both the specific integrated software, asystem called The Knowledge Factory, and of the general valueof machine learning for knowledge acquisition.