Skip to main content
padlock icon - secure page this page is secure

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

Buy Article:

$107.19 + tax (Refund Policy)

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 datarebalancing 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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics


Document Type: Research Article

Publication date: August 1, 2016

More about this publication?
  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more