Computational intelligence methods for rule-based data understanding
By Wlodzislaw Duch, Rudy Setiono, J. Zurada. 2004
231 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Tameru Hailesilassie
- Nahla H. Barakat
- Andrew P. Bradley
- Durgesh Kumar Mishra
- Kamal K. Sethi
- Bharat Mishra
- Nadia Burkart
- Marco F. Huber
In Computational Theory and Mathematics
- Tameru Hailesilassie
- Andrew P. Bradley
- Nahla H. Barakat
- Durgesh Kumar Mishra
- Kamal K. Sethi
- Bharat Mishra
- Nadia Burkart
- Marco F. Huber
In Information Systems
- Tameru Hailesilassie
- Durgesh Kumar Mishra
- Kamal K. Sethi
- Bharat Mishra
- Andrew P. Bradley
- Nahla H. Barakat
- Nadia Burkart
- Marco F. Huber
Citations in the corpus
- Rule Extraction Algorithm for Deep Neural Networks: A Review (2016, background): “Notwithstanding neural network attains high classification performance, they are not easily understandable [10].”
- Extended Taxonomy of Rule Extraction Techniques and Assessment of KDRuleEx (2012, background): “Duch has speculated that most research papers on the rule extraction are usually limited to the description of new algorithms and only partial solutions to the problem of rule extraction [16].”
- Rule Extraction from Support Vector Machines: A Sequential Covering Approach (2007, background): “In particular, Duch and Hayashi [18] and Duch et al. [19] suggest that rule sets can only offer explanation if the number of rules in the rule set is relatively small and its classification accuracy is high.”