Extracting rules from multilayer perceptrons in classification problems: A clustering-based approach
By Eduardo R. Hruschka, Nelson Francisco Favilla Ebecken. 2006
94 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
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
- Thangairulappan Kathirvalavakumar
- Gethsiyal Augasta M
- Vaishak Belle
- Ioannis Papantonis
- Lale Özbakır
- Sinem Kulluk
- Adil Baykasoglu
In Computational Theory and Mathematics
- Tameru Hailesilassie
- Vaishak Belle
- Ioannis Papantonis
- Thangairulappan Kathirvalavakumar
- Gethsiyal Augasta M
- Leandro Nunes de Castro
- Lale Özbakır
- Sinem Kulluk
Citations in the corpus
- Rule Extraction Algorithm for Deep Neural Networks: A Review (2016, methodology): “Hruschka and Ebecken [30] have proposed an algorithm which is based on an algorithm called RX proposed by Lu, Setiono, and Liu [31].”
- Fundamentals of natural computing: an overview (2007, background): “Among open problems there is knowledge extraction [143,149], the modeling of higher human cognitive abilities, interaction between emotional and conceptual, and the unification of language and cognition in evolving and integrated systems [227].”
- Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems (2012, methodology): “So the proposed method is also compared with one eclectic approach method namely Rx+CGA [42].”
- Principles and Practice of Explainable Machine Learning (2020, methodology): “RX [38] is such a method, based on clustering the hidden units of a NN and extracting logical rules connecting the input to the resulting clusters.”
- Rule extraction from neural networks — A comparative study (2012, methodology): “Hruschka and Ebecken [34] have proposed a method Rex-CGA to extract rules from multilayer perceptrons.”
- A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner (2010, methodology): “Hruschka and Ebecken [16] presented a clusteringbased approach for extracting rules from ANNs in classification problems.”
- Rule extraction from trained adaptive neural networks using artificial immune systems (2009, background): “On the other hand, some papers have proposed algorithms mainly intended to clarify the knowledge encoded in previously trained ANNs (Hruschka & Ebecken, 2006).”