C4.5: programs for machine learning
C4.5: programs for machine learning
Statistical inference and data mining
Communications of the ACM
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Decision Rule for Pattern Classification by Integrating Interval Feature Values
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Fuzzy Logic in Knowledge-Based Systems, Decision and Control
Fuzzy Logic in Knowledge-Based Systems, Decision and Control
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A Classification and Relationship Extraction Scheme for Raltional Databases Based on Fuzzy Logic
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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An important open issue in KDD research is the reveal and the handling of uncertainty. The popular classification approaches do not take into account this feature while they do not exploit properly the significant amount of information included in the results of classification process (i.e., classification scheme), though it will be useful in decision-making. In this paper we present a framework that maintains uncertainty throughout the classification process by maintaining the classification belief and moreover enables assignment of an item to multiple classes with a different belief. Decision support tools are provided for decisions related to: i. relative importance of classes in a data set (i.e., "young vs. old customers"), ii. relative importance of classes across data sets iii. the information content of different data sets. Finally we provide a mechanism for evaluating classification schemes and select the scheme that best fits the data under consideration.