ITER: An algorithm for predictive regression rule extraction. Data warehousing and knowledge discovery. Proceedings
AbstractVarious benchmarking studies have shown that artificial neural networks and support vector machines have a superior performance when compared to more traditional machine learning techniques. The main resistance against these newer techniques is based on their lack of interpretability: it is difficult for the human analyst to understand the motivation behind these models' decisions. Various rule extraction techniques have been proposed to overcome this opacity restriction. However, most of these extraction techniques are devised for classification and only few algorithms can deal with regression problems.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Katholieke Universiteit Leuven in its series Open Access publications from Katholieke Universiteit Leuven with number urn:hdl:123456789/101502.
Date of creation: 2006
Date of revision:
Publication status: Published in Lecture notes in computer science (2006) v.4081, p.270-279
Contact details of provider:
Web page: http://www.kuleuven.be
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Carl Demeyere).
If references are entirely missing, you can add them using this form.