Balancing Performance Measures in Classification Using Ensemble Learning Methods

Neeraj Bahl, Ajay Bansal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Ensemble learning methods have recently been widely used in various domains and applications owing to the improvements in computational efficiency and distributed computing advances. However, with the advent of wide variety of applications of machine learning techniques to class imbalance problems, further focus is needed to evaluate, improve and balance other performance measures such as sensitivity (true positive rate) and specificity (true negative rate) in classification. This paper demonstrates an approach to evaluate and balance the performance measures (specifically sensitivity and specificity) using ensemble learning methods for classification that can be especially useful in class imbalanced datasets. In this paper, ensemble learning methods (specifically bagging and boosting) are used to balance the performance measures (sensitivity and specificity) on a diabetes dataset to predict if a patient will be readmitted to the hospital based on various feature vectors. From the experiments conducted, it can be empirically concluded that, by using ensemble learning methods, although accuracy does improve to some margin, both sensitivity and specificity are balanced significantly and consistently over different cross validation approaches.

Original languageEnglish (US)
Title of host publicationBusiness Information Systems - 22nd International Conference, BIS 2019, Proceedings
EditorsWitold Abramowicz, Rafael Corchuelo
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783030204815
StatePublished - Jan 1 2019
Event22nd International Conference on Business Information Systems, BIS 2019 - Seville, Spain
Duration: Jun 26 2019Jun 28 2019

Publication series

NameLecture Notes in Business Information Processing
ISSN (Print)1865-1348


Conference22nd International Conference on Business Information Systems, BIS 2019


  • Balancing
  • Boosting
  • Classification
  • Ensemble methods

ASJC Scopus subject areas

  • Management Information Systems
  • Control and Systems Engineering
  • Business and International Management
  • Information Systems
  • Modeling and Simulation
  • Information Systems and Management


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