Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Classifier systems and genetic algorithms
Machine learning: paradigms and methods
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Managing Uncertainty in Expert Systems
Managing Uncertainty in Expert Systems
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This paper focuses on a performance comparison of two rule matching (classification) methods, used in data mining systems AQ15 and LERS. All rule sets used in our experiments were induced by the LERS (Learning from Examples using Rough Sets) system from ten typical input data sets. Then these rule sets were truncated using three different criteria: t-weight, u-weight and the strongest rule. The truncation process was performed using six different cut-off values for t-weight, six different cut-off values for u-weight and using the strongest rule option. Hence for each of the input rule files thirteen truncated rule sets were created. Performance was measured by a classification error rate. The objective of this study was to determine the best overall method of classification and the best truncation option.