Nikos Fazakis

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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)
Nowadays, more and more algorithms from fields of machine learning (ML) and artificial intelligence (AI) have been arisen by many researchers from several scientific domains. Although the origins of the specific problems that these algorithms confront may be extremely differentiated or seem absolutely heterogeneous, they can still be arranged into a small(More)
Semi-supervised classification methods are based on the use of unlabeled data in combination with a smaller set of labeled examples, in order to increase the classification rate compared with the supervised methods, in which the total training is executed only by the usage of labeled data. In this work, a self-train Logitboost algorithm is presented. 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)
Prediction of potential fraudulent activities may prevent both the stakeholders and the appropriate regulatory authorities of national or international level from being deceived. The objective difficulties on collecting adequate data that are obsessed by completeness affects the reliability of the most supervised Machine Learning methods. This work examines(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)