Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Machine Learning
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Using Rule Sets to Maximize ROC Performance
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Classification with Intersecting Rules
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Resolving rule conflicts with double induction
Intelligent Data Analysis
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part IV
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Rule conflicts can arise in machine learning systems that utilise unordered rule sets. A rule conflict is when two or more rules cover the same example but differ in their majority classes. This conflict must be solved before a classification can be made. The standard methods for solving this type of problem are to use naive Bayes to solve the conflict or using the most frequent class (CN2). This paper studies the problem of rule conflicts in the area of numerical features. A novel family of methods, called distance based methods, for solving rule conflicts in continuous domains is presented. An empirical evaluation between a distance based method, CN2 and naive Bayes is made. It is shown that the distance based method significantly outperforms both naive Bayes and CN2.