Knowledge of extraction from trained neural network by using decision tree

• Main Authors: Soleh, Ardiansyah, Mazlina, Abdul Majid, Jasni, Mohamad Zain
• Format: Conference or Workshop Item
• Language: English; English
• Published: IEEE 2017
• Subjects: QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/18263/1/Knowledge%20of%20Extraction%20from%20Trained%20Neural%20Network%20by%20Using%20Decision%20Tree.pdf; http://umpir.ump.edu.my/id/eprint/18263/2/Knowledge%20of%20Extraction%20from%20Trained%20Neural%20Network%20by%20Using%20Decision%20Tree%201.pdf; http://umpir.ump.edu.my/id/eprint/18263/; https://ieeexplore.ieee.org/document/7852637/
• Institution: Universiti Malaysia Pahang

Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge are described within topology, using activation function and connection weight at hidden neurons and output neurons. Is hardly to be understanding since neural networks act as a black box. The black bo...