Solving the Under-Fitting Problem for Decision Tree Algorithms by Incremental Swarm Optimization in Rare-Event Healthcare Classification

Healthcare data are well-known to be imbalanced in the data distribution of target classes where the samples of interest are much fewer than the ordinary samples. When it comes to healthcare data classification, insufficient supervised training in decision tree induction is prone to happen, leading to poor classification/prediction accuracy. Swarm Balancing Algorithm (SBA) was proposed to optimize the parameter values of a popular data-rebalancing method called Synthetic Minority Over-sampling Technique (SMOTE) for rectifying the under-fitting problems. Though it works well, the drawback of SBA is the requirement that all the data must be initially available. In this paper, an alternative approach which extends from SBA, namely, Incremental Swarm Balancing Algorithm (ISBA) is investigated on the impacts of decision trees. ISBA obtains higher classification accuracy at faster speed than SBA by optimizing SMOTE and training a decision tree on the fly. In our design, two swarm algorithms, particle swarm optimization and bat-inspired algorithm, are used to couple with two different types of decision tree classifiers, Decision Tree (DT) and Hoeffding Tree (HT). The former represents the traditional batch-type decision tree model, and the latter is typical incremental decision tree model. Experimentation over two sets of imbalanced healthcare data is performed, with the aim of comparing and contrasting the efficacy of ISBA for DT and HT.