title: imbalanced data classification with boosting algorithms
body: <p>I am working on a binary data classification problem. The dataset is imbalanced, it consists of 92% 'false' labels and 8% 'true' labels. The number of features is 18 and I have a small number of 650 data points. I want to use boosting algorithms in matlab like 'GentleBoost' to solve this problem. I assign uniform for prior as follows:</p>
  
  <p>ada = fitensemble(Xtrain,Ytrain,'GentleBoost',10,'Tree','LearnRate',0.1, 'prior', 'uniform');</p>
  
  <p>but the performance is consistently poor. How should I set the parameters? Is it necessary to set a cost? How can I do this?Is there any classifier that perform better than this? </p>