On the induction of decision trees for multiple concept learning
On the induction of decision trees for multiple concept learning
Machine Learning
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MIS Quarterly
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Sequential Decision Models for Expert System Optimization
IEEE Transactions on Knowledge and Data Engineering
Debiasing Training Data for Inductive Expert System Construction
IEEE Transactions on Knowledge and Data Engineering
Implementing a data mining solution for enhancing carpet manufacturing productivity
Knowledge-Based Systems
Rule-based data mining for yield improvement in semiconductor manufacturing
Applied Intelligence
Expert Systems with Applications: An International Journal
Predictive collaborative performance system in B2B supply chain using neuro-fuzzy
ICOSSSE'10 Proceedings of the 9th WSEAS international conference on System science and simulation in engineering
Optimized fuzzy decision tree data mining for engineering applications
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Improving quality control by early prediction of manufacturing outcomes
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Expert Systems with Applications: An International Journal
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The generalized ID3 (GID3) algorithm, which takes a training set of experimental data and produces a decision tree that predicts the outcome of future experiments under various, more general conditions, is described. The tree can then be translated into a set of rules for an expert system. Two extensions to GID3MmRIST, and KARSM-that deal with the problems of noisy data and the limited availability of training data are discussed. The application of GID3 to reactive ion etching manufacturing process diagnosis and optimization and to knowledge acquisition for an expert system is described.