יובל אלוביץ

Feature selection is an essential process for machine learning tasks since it improves generalization capabilities, and reduces run-time and a model's complexity. In many applications, the cost of collecting the features must be taken into account. To cope with the cost problem, we developed a new cost-sensitive fitness function based on histogram comparison. This function is integrated with a genetic search method to form a new feature selection algorithm termed CASH (cost-sensitive attribute selection algorithm using histograms). The CASH algorithm takes into account feature collection costs as well as feature grouping and misclassification costs. Our experiments in various domains demonstrated the superiority of CASH over several other cost-sensitive genetic algorithms.