C4.5: programs for machine learning
C4.5: programs for machine learning
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Mining IC test data to optimize VLSI testing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining solves tough semiconductor manufacturing problems
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Data mining for process and quality control in the semiconductor industry
Data mining for design and manufacturing
Efficient GA Based Techniques for Classification
Applied Intelligence
Generalization Bounds for Decision Trees
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Information Sciences: an International Journal - Special issue: Soft computing data mining
Feature set decomposition for decision trees
Intelligent Data Analysis
Collective-agreement-based pruning of ensembles
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Rule-based data mining for yield improvement in semiconductor manufacturing
Applied Intelligence
Visual analysis of quality-related manufacturing data using fractal geometry
Journal of Intelligent Manufacturing
| Hi-index | 0.00 |
Data mining methods can be used for discovering interesting patterns in manufacturing databases. These patterns can be used to improve manufacturing processes. However, data accumulated in manufacturing plants usually suffer from the 'Curse of Dimensionality', that is, relatively small number of records compared to large number of input features. As a result, conventional data mining methods may be inaccurate in these cases. This paper presents a new feature set decomposition approach that is based on genetic algorithm. For this purpose a new encoding schema is proposed and its properties are discussed. Moreover we examine the effectiveness of using a Vapnik-Chervonenkis dimension bound for evaluating the fitness function of multiple oblivious trees classifiers. The new algorithm was tested on various real-world manufacturing data sets. The results obtained have been compared to other methods, indicating the superiority of the proposed algorithm.