TY - GEN
T1 - Using significant, positively associated and relatively class correlated rules for associative classification of imbalanced datasets
AU - Verhein, Florian
AU - Chawla, Sanjay
PY - 2007
Y1 - 2007
N2 - 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, SPAR-CCC 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.
AB - 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, SPAR-CCC 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.
UR - http://www.scopus.com/inward/record.url?scp=49749113225&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2007.63
DO - 10.1109/ICDM.2007.63
M3 - Conference contribution
AN - SCOPUS:49749113225
SN - 0769530184
SN - 9780769530185
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 679
EP - 684
BT - Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
T2 - 7th IEEE International Conference on Data Mining, ICDM 2007
Y2 - 28 October 2007 through 31 October 2007
ER -