With the recent growth of Internet, volume data has also increased. In particular, increase in amount unstructured makes it difficult to manage data. Classification is needed order be able use for various purposes. Since manually classify ever-increasing purpose types analysis and evaluation, automatic classification methods are needed. addition, performance imbalanced multi-class a challenging...

We propose a Polylingual text Embedding (PE) strategy, that learns a language independent representation of texts using Neural Networks. We study the effects of bilingual representation learning for text classification and we empirically show that the learned representations achieve better classification performance compared to traditional bag-of-words and other monolingual distributed represen...

Fuzzy Random Forests are well-known Machine Learning ensemble methods. They combine the outputs of multiple Decision Trees to improve classification performance. Moreover, they can deal with data uncertainty and imprecision thanks use fuzzy logic. Although many tasks binary, in some situations we face problem classifying into a set ordered categories. This is particular case multi-class where o...

Ordinal regression problem and general multi-class classification problem are important and on-going research subject in machine learning. Support vector ordinal regression machine (SVORM) is an effective method for ordinal regression problem and has been used to deal with general multi-class classification problem. Up to now it is always assumed implicitly that the training data are known exac...

We address the problem of multi-class classification in the case where the number of classes is very large. We propose a double sampling strategy on top of a multi-class to binary reduction strategy, which transforms the original multi-class problem into a binary classification problem over pairs of examples. The aim of the sampling strategy is to overcome the curse of long-tailed class distrib...