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Particle Swarm Optimization algorithm (PSO) has been largely studied over the years due to its flexibility and competitive results in different applications. Nevertheless, its performance depends on different aspects of design (e.g., inertia factor, velocity equation, topology). The task of deciding which is the best algorithm design to solve a particular problem is challenging due to the great number...
Particle Swarm Optimization (PSO) is largely used to solve optimization problems effectively. Nonetheless, the PSO performance depends on the fine tuning of different parameters. To make the algorithm design process more independent from human intervention, some researchers have treated this task as an optimization problem. Grammar-guided Genetic Programming algorithms (GGGP), in special, have been...
Group profiling methods aim to construct a descriptive profile for communities in complex networks. The application of such methods in the analysis of co-authorship networks enables us to move forward in understanding the scientific communities, leading to new approaches to strengthen and expand scientific collaboration networks. This task is similar to the document cluster labeling task, which encourages...
Classification is one of the most traditional tasks in machine learning. In supervised learning for classification, the goal is to learn a classifier function using a completely labeled dataset. Semi-supervised learning modifies the learning algorithm function allowing the use of partially labeled data. Single-label classification assigns only one label to each instance in the dataset, while multi-label...
During the software testing process a variety of test suites can be generated in order to evaluate and assure the quality of the products. However, in some contexts the execution of all suites does not fit the available resources (time, people, etc). In such cases, the suites could be automatically reduced based on some selection criterion. Automatic Test Case (TC) selection could be used to reduce...
The emergence of online social networks has generated an enormous amount of data containing users' opinions about the most varied subjects. Aiming to identify opinion orientation, Sentiment Analysis techniques have been proposed, mainly based on text classification methods. We propose a different perspective to treat this problem, based on a user centric approach. We adopt a graph representation in...
Support Vector Machines (SVMs) have become a well succeeded technique due to the good performance it achieves on different learning problems. However, the SVM performance depends on adjustments of its parameters' values. The automatic SVM parameter selection is treated by many authors as an optimization problem whose goal is to find a suitable configuration of parameters for a given learning problem...
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