Relationship between Diversity and Perfomance of Multiple Classifiers
  for Decision Support
  The paper presents the investigation and implementation of the relationship
between diversity and the performance of multiple classifiers on classification
accuracy. The study is critical as to build classifiers that are strong and can
generalize better. The parameters of the neural network within the committee
were varied to induce diversity; hence structural diversity is the focus for
this study. The hidden nodes and the activation function are the parameters
that were varied. The diversity measures that were adopted from ecology such as
Shannon and Simpson were used to quantify diversity. Genetic algorithm is used
to find the optimal ensemble by using the accuracy as the cost function. The
results observed shows that there is a relationship between structural
diversity and accuracy. It is observed that the classification accuracy of an
ensemble increases as the diversity increases. There was an increase of 3%-6%
in the classification accuracy.
