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Normas de associação semântica, embora recentes no Brasil, são pesquisadas a mais de um século internacionalmente, constituindo-se ferramentas importantes para tarefas de avaliação da memória e linguagem. Neste estudo 108 universitários responderam a primeira palavra que lhes viesse à mente com significado relacionado a cada uma das 88 palavras-alvo apresentadas. Foi analisada força de associação entre pares e tamanho do conjunto do alvo. A maioria dos pares apresentou forte ou média associação semântica. A média de tamanho total e significativo do conjunto foi, respectivamente 37,56 e 12,40. Observaram-se correlações negativas entre força de associação e tamanho do conjunto, total e significativo. A concretude dos alvos correlacionou-se positivamente com força de correlação com nehuma das outras variáveis analisadas. Pode-se inferir que quanto maior o tamanho do conjunto do alvo, menor a força de associação entre os pares

Abstract Background Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net. We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone. Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Results Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L1) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error. Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations. Conclusions The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters. The penalized SVM classification algorithms as well as fixed grid and interval search for finding appropriate tuning parameters were implemented in our freely available R package 'penalizedSVM'. We conclude that the Elastic SCAD SVM is a flexible and robust tool for classification and feature selection tasks for high-dimensional data such as microarray data sets.