Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Classifier systems and genetic algorithms
Machine learning: paradigms and methods
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Closest Fit Approach to Missing Attribute VAlues in Preterm Birth Data
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
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In many applications data are inconsistent: for two distinct cases attribute values are the same but the decisions are different. For example, two patients are characterized identically by all tests, demographic variables, etc., however, one delivers a baby prematurely and the other delivers at full term.Our main objective was to increase sensitivity, i.e., a conditional probability of true positives. In order to achieve this objective we changed a strength multiplier for rules describing preterm cases. In our experiments we induced rules from preterm birth data using LERS (Learning from Examples using Rough Sets). The problem was to make the best use of certain and possible rule sets induced by LERS from inconsistent data. To solve this problem we used eight different strategies: using only certain rules, only possible rules, first certain then possible rules, and both rule sets; combined with two different ways to use rules in complete and partial matching.