An experimental evaluation of integrating machine learning with knowledge acquisition

Machine learning and knowledge acquisition from experts have distinct capab ilities that appear to complement one another. We report a study that demon strates the integration of these approaches can both improve the accuracy o f the developed knowledge base and reduce development time. In addition, we found that users expected the expert systems created through the integrate d approach to have higher accuracy than those created without machine learn ing and rated the integrated approach less difficult to use. They also prov ided favorable evaluations of both the specific integrated software, a syst em called The Knowledge Factory, and of the general value of machine learni ng for knowledge acquisition.