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Multi-label classification is a machine learning task where instances can be classified into two or more labels simultaneously. In this task, there exist correlations between the instances belonging to same or similar sets of labels. This paper proposes the incorporation of instance correlations by modifying the multi-label datasets. We used the label-space to create new features, which represent...
Misleading diagnosis of skin diseases can result in complications during the healing process. Skin images provide important information for the medical staff for information storage and exchange, to trying to prevent this misdiagnosis from happening. For such, a good segmentation process is needed. The segmentation of these images is already being used and has been an effective tool for skin diseases...
Supervised classification is the most studied task in Machine Learning. Among the many algorithms used in such task, Decision Tree algorithms are a popular choice, since they are robust and efficient to construct. Moreover, they have the advantage of producing comprehensible models and satisfactory accuracy levels in several application domains. Like most of the Machine Leaning methods, these algorithms...
There are many algorithms for image segmentation, but there is no optimal algorithm for all kind of image applications. To recommend an adequate algorithm for segmentation is a challenging task that requires knowledge about the problem and algorithms. Meta-learning has recently emerged from machine learning research field to solve the algorithm selection problem. This paper applies meta-learning to...
Many classification algorithms, such as Neural Networks and Support Vector Machines, have a range of hyper-parameters that may strongly affect the predictive performance of the models induced by them. Hence, it is recommended to define the values of these hyper-parameters using optimization techniques. While these techniques usually converge to a good set of values, they typically have a high computational...
Classification is one of the most common machine learning tasks. SVMs have been frequently applied to this task. In general, the values chosen for the hyper-parameters of SVMs affect the performance of their induced predictive models. Several studies use optimization techniques to find a set of hyper-parameter values that induces classifiers with good predictive performance. This paper investigates...
The paper focuses on the problem of methods and tools to assist the specification generation, proposal generation and design of automated production systems. The solutions adopted use meta-modeling techniques, web services, hierarchical models, and mechatronic model of plant components integrating four different views of the systems, namely mechanical, electrical, hydro-pneumatic and control ones...
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