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The most important asset of semisupervised classification methods is the use of available unlabeled data combined with a clearly smaller set of labeled examples, so as to increase the classification accuracy compared with the default procedure of supervised methods, which on the other hand use only the labeled data during the training phase. Both the(More)
Adoption of techniques from fields related with Data Science, such as Machine Learning, Data Mining and Predictive Analysis, in the task of bankruptcy prediction can produce useful knowledge for both the policy makers and the organizations that are already funding or are interested in acting towards this direction in the near future. The nature of this task(More)
Exploiting both labeled and unlabeled instances of various problems seems a really promising strategy, since useful information that is contained on the latter pool of data is discarded during supervised approaches. However, the size of the unlabeled data that needs to be examined is usually extremely large and efficient algorithms should be utilized in(More)