Using significant, positively associated and relatively class correlated rules for associative classification of imbalanced datasets

Florian Verhein, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

33 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
Pages679-684
Number of pages6
DOIs
StatePublished - 2007
Externally publishedYes
Event7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE, United States
Duration: Oct 28 2007Oct 31 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference7th IEEE International Conference on Data Mining, ICDM 2007
Country/TerritoryUnited States
CityOmaha, NE
Period10/28/0710/31/07

Fingerprint

Dive into the research topics of 'Using significant, positively associated and relatively class correlated rules for associative classification of imbalanced datasets'. Together they form a unique fingerprint.

Cite this