document: Beyond classification and ranking : constrained optimization of the ROI Classification has been commonly used in many data mining projects in the financial service industry . For instance , to predict collectability of accounts receivable , a binary class label is created based on whether a payment is received within a certain period . However , optimization of the classifier does not necessarily lead to maximization of return on investment ( ROI ) , since maximization of the true positive rate is often different from maximization of the collectable amount which determines the ROI under a fixed budget constraint . The typical cost sensitive learning does not solve this problem either since it involves an unknown opportunity cost due to the budget constraint . Learning the ranks of collectable amount would ultimately solve the problem , but it tries to tackle an unnecessarily difficult problem and often results in poorer results for our specific target . We propose a new algorithm that uses gradient descent to directly optimize the related monetary measure under the budget constraint and thus maximizes the ROI . By comparison with several classification , regression , and ranking algorithms , we demonstrate the new algorithm 's substantial improvement of the financial impact on our clients in the financial service industry .
doc_bio_tags: O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O