A Parameter-Free Associative Classification Method
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Using Highly Expressive Contrast Patterns for Classification - Is It Worthwhile?
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
A study on interestingness measures for associative classifiers
Proceedings of the 2010 ACM Symposium on Applied Computing
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
Behavior-based clustering and analysis of interestingness measures for association rule mining
Data Mining and Knowledge Discovery
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The application of association rule mining to classification has led to a new family of classifiers which are often referred to as "Associative Classifiers (ACs)". An advantage of ACs is that they are rule-based and thus lend themselves to an easier interpretation. Rule-based classifiers can play a very important role in applications such as medical diagnosis and fraud detection where "imbalanced data sets" are the norm and not the exception. The focus of this paper is to extend and modify ACs for classification on imbalanced data sets using only statistical techniques. We combine the use of statistically significant rules with a new measure, the Class Correlation Ratio ( CCR), to build an AC which we call SPARCCC. Experiments show that in terms of classification quality, SPARCCC performs comparably on balanced datasets and outperforms other AC techniques on imbalanced data sets. It also has a significantly smaller rule base and is much more computationally efficient.