SSDT: a scalable subspace-splitting classifier for biased data

Decision trees are extensively used data mining models. Recently, a number of efficient, scalable algorithms for constructing decision trees on large disk-resident datasets have been introduced. In this paper, we study the problem of learning scalable decision trees from datasets with biased class distribution. Our objective is to build decision trees that are more concise and more interpretable while maintaining the scalability of the model. To achieve this, our approach searches for subspace clusters of data cases of the biased class to enable multivariate splittings based on weighted distances to such clusters. In order to build concise and interpretable models, other approaches including multivariate decision trees and association rules, often introduce scalability and performance issues. The SSDT algorithm we present achieves the objective without loss in efficiency, scalability, and accuracy