The SI-Combiner: Making sense of results from multiple trained programs
Bobi K. Den Hartog, John W. Elling, R. M. Kieckhafer · University of North Texas Digital Library (University of North Texas) · 1998
Many problems, such as Aroclor Interpretation, are ill-conditioned problems in which trained programs, or methods, must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. Importantly, when multiple trained methods fail divergently, their patterns of failures provide insights into the true results. The SI-Combiner solves this problem of Integrating Multiple learned Models (IMLM) by automatically learning and using these insights to produce a solution more accurate than any single trained program. In application, the Aroclor Interpretation SI-Combiner improved on the accuracy of the most accurate individual trained program in the suite. This paper presents a new fuzzy IMLM method called the SI-Combiner and its application to Aroclor Interpretation. Additionally, this paper shows the improvement in accuracy that the SI-Combiner`s components show against Multicategory Classification (MCC), Dempster-Shafer (DS), and the best individual trained program in the Aroclor Interpretation suite (iMLR).