Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Feature selection in unsupervised learning via evolutionary search
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
An effective way to represent quadtrees
Communications of the ACM
ACM Computing Surveys (CSUR)
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inside Case-Based Reasoning
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection with Selective Sampling
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
Multidimensional Binary Search Trees in Database Applications
IEEE Transactions on Software Engineering
A case-based reasoning system for PCB defect prediction
Expert Systems with Applications: An International Journal
Medical data mining by fuzzy modeling with selected features
Artificial Intelligence in Medicine
Efficiency measures of PCB manufacturing firms using relational two-stage data envelopment analysis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A forecasting solution to the oil spill problem based on a hybrid intelligent system
Information Sciences: an International Journal
Case-based polishing process planning with Fuzzy Set Theory
Journal of Intelligent Manufacturing
Case-based parametric design system for test turntable
Expert Systems with Applications: An International Journal
Using grey relation analysis and TOPSIS to measure PCB manufacturing firms efficiency in Taiwan
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Development of a soldering quality classifier system using a hybrid data mining approach
Expert Systems with Applications: An International Journal
Combining rough set and case based reasoning for process conditions selection in camshaft grinding
Journal of Intelligent Manufacturing
Literature review on the creativity of CBR applications
Artificial Intelligence Review
| Hi-index | 12.06 |
The Printed Circuit Board (PCB) manufacturing process usually consists of lengthy production activities. Each activity is controlled by a number of process parameters. Although numerous process parameters must be determined before fabrication, only a number of parameters called principal process parameters because they affect the quality of a PCB product. As long as the principal process parameters are identified efficiently and controlled well, the manufacturing lead-time can be shortened and the quality of the new PCB product can be assured. This research proposes a Case-Based Reasoning (CBR) system to infer the principal process parameters for a new PCB product. Each case in the case-base stores design specifications, process parameters, and the corresponding production quality specifications. A Significant Nearest Neighbor (SNN) search is developed to retrieve similar cases from a case-base. A Mutual Correlation Parameter Selection (MCPS) method and a correlation-based parameter setting method are developed to identify the principal parameters and infer their reasonable value range. A set of experiments and a practical implementation case are demonstrated to show the efficiency and accuracy of the proposed system.