KDRuleEx: A Novel Approach for Enhancing User Comprehensibility Using Rule Extraction
By Kamal K. Sethi, Durgesh Kumar Mishra, Bharat Mishra. 2012
34 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
- Eneldo Loza Mencía
- Frederik Janssen
- Jan Ruben Zilke
- Durgesh Kumar Mishra
- Kamal K. Sethi
- Bharat Mishra
- Marco F. Huber
In Computational Theory and Mathematics
- Tameru Hailesilassie
- Eneldo Loza Mencía
- Frederik Janssen
- Jan Ruben Zilke
- Durgesh Kumar Mishra
- Kamal K. Sethi
- Bharat Mishra
- Nadia Burkart
In Information Systems
- Tameru Hailesilassie
- Eneldo Loza Mencía
- Frederik Janssen
- Jan Ruben Zilke
- Durgesh Kumar Mishra
- Kamal K. Sethi
- Bharat Mishra
- Nadia Burkart
Citations in the corpus
- Rule Extraction Algorithm for Deep Neural Networks: A Review (2016, background): “Mishra [26] have proposed KDRuleEx that can generate a two-dimensional matrix of a decision table.”
- Extended Taxonomy of Rule Extraction Techniques and Assessment of KDRuleEx (2012, methodology): “KDRuleEx is a novel algorithm for Rule Extraction which extract transparent model in the form of decision table[10].”
- DeepRED - Rule Extraction from Deep Neural Networks (2016, methodology): “The works dealing with pedagogical rule extraction, for instance, comprise the VIA method discussed in [17, 18], different sampling-based approaches [5, 16, 13, 21, 14], and the RxREN algorithm [2].”