Defect prediction with neural networks
ANNA '91 Proceedings of the conference on Analysis of neural network applications
C4.5: programs for machine learning
C4.5: programs for machine learning
Applications of machine learning and rule induction
Communications of the ACM
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
An Enhanced Neural Network Technique for Software Risk Analysis
IEEE Transactions on Software Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Using SVM based method for equipment fault detection in a thermal power plant
Computers in Industry
Mining association rules for the quality improvement of the production process
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
This paper presents a nonlinear structural health inference technique, based on an interactive data mining approach. A mining control agent emulating cognitive process of human analysts is developed and integrated in the data mining loop, analyzing and verifying the output of the data miner and controlling the data mining process to improve the interaction between human users and computer system. Additionally, an artificial neural network method, which is adopted as a core component of the proposed interactive data mining method, is evolved by adding a novelty detecting and retraining function for handling complicated nuclear power plant quake-proof data. Based on proposed approach, an information inference system has been developed. To demonstrate how the proposed technique can be used as a powerful tool for inferring of structural health status in unclear power plant, quake-proof testing data have been applied.