Classification approach based on association rules mining for unbalanced data

TL;DR

This paper proposes a novel classification method for unbalanced data using association rules learning, which identifies patterns strongly correlated with the minority class to improve sensitivity over traditional classifiers.

Contribution

It introduces a supervised association rules-based approach for unbalanced classification, enhancing detection of rare target classes compared to standard methods.

Findings

Improved sensitivity for minority class detection.

Effective pattern identification via association rules.

Outperforms traditional classifiers in unbalanced scenarios.

Abstract

This paper deals with the binary classification task when the target class has the lower probability of occurrence. In such situation, it is not possible to build a powerful classifier by using standard methods such as logistic regression, classification tree, discriminant analysis, etc. To overcome this short-coming of these methods which yield classifiers with low sensibility, we tackled the classification problem here through an approach based on the association rules learning. This approach has the advantage of allowing the identification of the patterns that are well correlated with the target class. Association rules learning is a well known method in the area of data-mining. It is used when dealing with large database for unsupervised discovery of local patterns that expresses hidden relationships between input variables. In considering association rules from a supervised learning point of view, a relevant set of weak classifiers is obtained from which one derives a classifier that performs well.