Cost Sensitive Boosting Performance Evaluation for Classifying Underdeveloped Regions in Indonesia Bayu Suseno, Bagus Sartono, Khairil Anwar Notodiputro
IPB University
Abstract
Imbalanced classes are indicated by having more instances of some classes than others. The cost-Sensitive boosting algorithm is a modification of the AdaBoost algorithm, which aims to solve the problem of imbalanced classes. In this study, we evaluate the cost-sensitive Boosting algorithm AdaC2 using Indonesia^s underdeveloped region^s data. This study confirms that the cost-sensitive boosting algorithm (AdaC2) performs better in classifying the instances in minority classes than ^standards^ classification method algorithms such as Single Decision Tree, Random Forest, and AdaBoost.
