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

A Heuristic Scalable Classifier Ensemble of Binary Classifier Ensembles

Buy Article:

$106.34 + tax (Refund Policy)

Although a better performance for classifier is defined the more accurate classifier, but turning to the best classifier is not always the best option to obtain the best quality in classification. It means to reach the best classification there is another alternative to use many inaccurate or weak classifiers each of them is specialized for a sub-space in the problem space and using their consensus vote as the final classifier. So this paper proposes a heuristic classifier ensemble to improve the performance of classification learning. It is specially deal with multiclass problems which their aim is to learn the boundaries of each class from many other classes. Based on the concept of multiclass problems classifiers are divided into two different categories: pairwise classifiers and multiclass classifiers. The aim of a pairwise classifier is to separate one class from another one. Because of pairwise classifiers just train for discrimination between two classes, decision boundaries of them are simpler and more effective than those of multiclass classifiers. The main idea behind the proposed method is to focus classifier in the erroneous spaces of problem and use of pairwise classification concept instead of multiclass classification concept. Indeed although usage of pairwise classification concept instead of multiclass classification concept is not new, we propose a new pairwise classifier ensemble with a very lower order. In this paper, first the most confused classes are determined and then some ensembles of classifiers are created. The classifiers of each of these ensembles jointly work using majority weighting votes. The results of these ensembles are combined to decide the final vote in a weighted manner. Finally the outputs of these ensembles are heuristically aggregated. The proposed framework is evaluated on a very large scale Persian digit handwritten dataset and the experimental results show the effectiveness of the algorithm.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: GENETIC ALGORITHM; MULTICLASS CLASSIFICATION; OPTICAL CHARACTER RECOGNITION; PAIRWISE CLASSIFIER

Document Type: Research Article

Publication date: December 1, 2012

More about this publication?
  • Journal of Bioinformatics and Intelligent Control (JBIC) is an international journal that publishes research articles in areas of the bioinformatics and intelligent control. JBIC is aimed to provide an international forum for the exchange of ideas and new scientific and technological findings to disseminate information and promote the transfer of knowledge between professionals in academia and industry. The journal publishes original research papers; review papers; technical reports and notes; short communications focused on emerging new developments in these research areas.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Aims & Scope
  • 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
X
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