Recursive Tube-Partitioning Algorithm for a Class Imbalance Problem

A standard classifier acquired from a machine learning literature aims to categorize an instance into a well-defined class having comparable number of instances while data from real world problems tend to be imbalance. One way to deal with this imbalance problem is to modify the standard classification algorithm to capture minority instances and majority instances simultaneously. This work modifies the recursive partitioning algorithm based on a set of tubes, called the tube-tree algorithm. A tube-tree is a collection of tubes building from the combination of the input attributes where an internal node contains distinct class tubes corresponding to their respective classes. A tube composes of three components: a core vector, a tube length, and a tube radius built for each class regardless of its size suitable for an imbalance dataset. The forty six experiments are derived from the KEEL repository to compare the performance of the tube-tree with the support vector machine, the decision tree from C4.5, the decision tree from C5.0, and the naive Bayes classifier. The results of the tube-tree show the improvement over other classifiers of recall, and F1-measure except precision via the Wilcoxon signed rank test.