Addressing the class imbalance problem is critical for several real world applications. The application of pre-processing methods is a popular way of dealing with this problem. These solutions increase the rare class examples and/or decrease the …
The class imbalance problem has been thoroughly studied over the past two decades. More recently, the research community realized that the problem of imbalanced distributions also occurred in other tasks beyond classification. Regression problems are …
The problem of imbalanced domains is important in multiple real world applications. This problem has been thoroughly studied for classification tasks. In particular, the adaptation of ensembles to tackle imbalanced domains has shown important …
The problem of imbalanced domains, framed within predictive tasks, is relevant in many practical applications. When dealing with imbalanced domains a performance degradation is usually observed on the most rare and relevant cases for the user. This …
Proceedings of Machine Learning Research 94:1–7, 2018LIDTA 20182nd Workshop onLearning with Imbalanced Domains: PrefaceLu ́ıs Torgoltorgo@dal.caFaculty of Computer Science, Dalhousie UniversityHalifax, CanadaStan Matwinstan@cs.dal.caFaculty of …
This volume contains the Proceedings of the International Workshop on Cost-SensitiveLearning - COST’2018. This workshop was co-organized by the Laboratory of ArtificialIntelligence and Decision Support (INESC TEC), the Department …
Ensemble methods are well known for providing an advantage over single models in a large range of data mining and machine learning tasks. Their benefits are commonly associated to the ability of reducing the bias and/or variance in learning tasks. …
Imbalanced domains are an important problem that arises in predictive tasks causing a loss in the performance of the most relevant cases for the user. This problem has been intensively studied for classification problems. Recently it was recognized …
Accounting for misclassification costs is important in many practical applications of machine learning, and cost-sensitive techniques for classification have been studied extensively. Utility-based learning provides a generalization of purely …
This volume contains the Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications - LIDTA2017. This Workshop is co-organised by the Laboratory of Artificial Intelligence and Decision Support - …