Rule Extraction from Neural Network by Genetic Algorithm with Pareto Optimization

By Urszula Markowska-Kaczmar, Pawel Wnuk-Lipinski. 2004

25 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

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

1. Willem F. G. Haselager
2. Marcel A. J. van Gerven
3. Lale Özbakır
4. Sinem Kulluk
5. Adil Baykasoglu
6. Gabrielle Ras
7. Ana Marasovic
8. Sarah Wiegreffe

In Control and Systems Engineering

1. Marcel A. J. van Gerven
2. Lale Özbakır
3. Sinem Kulluk
4. Adil Baykasoglu
5. Gabrielle Ras

Citations in the corpus

• Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges (2018, background): “This term appears in (Arbatli and Akin, 1997; Markowska-Kaczmar and Wnuk-Lipi´nski, 2004; Zilke et al., 2016; Ribeiro et al., 2016a,b; Andrews et al., 1995; Lakkaraju et al., 2017).”
• A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner (2010, methodology): “Markowska-Kaczmar and WnukLipinski [29] proposed an approach for rule extraction from an ANN based on the genetic approach with Pareto optimization.”