Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks
Diederich, Joachim, Tickle, Alan B., & Geva, Shlomo (2010) Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks. In Advances in Machine Learning I. Springer, Berlin ; Heidelberg, pp. 479-490.
Rule extraction from neural network algorithms have been investigated for two decades and there have been significant applications. Despite this level of success, rule extraction from neural network methods are generally not part of data mining tools, and a significant commercial breakthrough may still be some time away. This paper briefly reviews the state-of-the-art and points to some of the obstacles, namely a lack of evaluation techniques in experiments and larger benchmark data sets. A significant new development is the view that rule extraction from neural networks is an interactive process which actively involves the user. This leads to the application of assessment and evaluation techniques from information retrieval which may lead to a range of new methods.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Book Chapter|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
Current > Institutes > Institute for Future Environments
|Deposited On:||19 Jan 2012 12:33|
|Last Modified:||01 Mar 2012 00:30|
Repository Staff Only: item control page