Advances in cost-sensitive multiclass and multilabel classification
Journal
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages
3187-3188
Date Issued
2019
Author(s)
Abstract
Classification is an important problem for data mining and knowledge discovery and comes with a wide range of applications. Different applications usually evaluate the classification performance with different criteria. The variety of criteria calls for cost-sensitive classification algorithms, which take the specific criterion as input to the learning algorithm and adapt to different criteria more easily. While the cost-sensitive binary classification problem has been relatively well-studied, the cost-sensitive multiclass and multilabel classification problems are harder to solve because of the sophisticated nature of their evaluation criteria. The tutorial aims to review current techniques for solving cost-sensitive multiclass and multilabel classification problems, with the hope of helping more real-world applications enjoy the benefits of cost-sensitive classification.
Type
conference paper