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  • Alberto Fernández; Salvador García; Mikel Galar; Ronaldo C. Prati; Bartosz Krawczyk; Francisco Herrera
    978-3-319-98074-4
    2018
    Edition 1
    • Offers a comprehensive review of imbalanced learning widely used worldwide in many real applications, such as fraud detection, disease diagnosis, etc
    • Provides the user with the required background and software tools needed to deal with Imbalance data
    • Presents the latest advances in the field of learning with imbalanced data, including Big Data applications and non-classical problems, such as semi-supervised learning, multilabel and multi instance learning, and ordinal classification and regression
    • Includes case studies

    €280

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