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Breast cancer (BC) is a deadly disease, killing millions of people every year. Developing automated malignant BC detection system applied on patient's imagery can help dealing with this problem more efficiently, making diagnosis more scalable and less prone to errors. Not less importantly, such kind of research can be extended to other types of cancer, making even more impact to help saving lives...
This paper proposes a classification approach in which monolithic and multiple classifier systems are combined in a cascading fashion. The rationale behind that is to deal with the existing trade-off between the need for increasing the accuracy, while reducing the complexity of the classification method. In other words, the idea is to offer an interesting strategy to conciliate the different levels...
This work describes the Dynse framework, which uses dynamic selection of classifiers to deal with concept drift. Basically, classifiers trained on new supervised batches available over time are add to a pool, from which is elected a custom ensemble for each test instance during the classification time. The Dynse framework is highly customizable, and can be adapted to use any method for dynamic selection...
Different dynamic classifier selection techniques have been proposed in the literature to determine among diverse classifiers available in a pool which should be used to classify a test instance. The individual competence of each classifier in the pool is usually evaluated taking into account its accuracy on the neighborhood of the test instance in a validation dataset. In this work we investigate...
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