A New Diversity Technique for Imbalance Learning Ensembles

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Data mining and machine learning techniques designed to solve classification problems require balanced class distribution. However, in reality sometimes the classification of datasets indicates the existence of a class represented by a large number of instances whereas there are classes with far fewer instances. This problem is known as the class imbalance problem. Classifier Ensembles is a method often used in overcoming class imbalance problems. Data Diversity is one of the cornerstones of ensembles. An ideal ensemble system should have accurrate individual classifiers and if there is an error it is expected to occur on different objects or instances. This research will present the results of overview and experimental study using Hybrid Approach Redefinition (HAR) Method in handling class imbalance and at the same time expected to get better data diversity. This research will be conducted using 6 datasets with different imbalanced ratios and will be compared with SMOTEBoost which is one of the Re-Weighting method which is often used in handling class imbalance. This study shows that the data diversity is related to performance in the imbalance learning ensembles and the proposed methods can obtain better data diversity.

     

     


  • Keywords


    Class Imbalance, Classifier Ensembles. Data Diversity, Hybrid Approach Redefinition

  • References


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Article ID: 11251
 
DOI: 10.14419/ijet.v7i2.11251




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