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...

The binary support vector machines (SVMs) have been extensively investigated. However their extension to a multi-classification model is still an on-going research. In this paper we present an extension of the binary support vector machines (SVMs) for the k > 2 class problems. The SVM model as originally proposed requires the construction of several binary SVM classifiers to solve the multi-cla...

Recently several methods were proposed for the improvement of multi-label classification performance by using constraints on labels. Such constraints are based on dependencies between classes often present in multi-label data and can be mined as association rules from training data. The rules are then applied in a post-processing step to correct the classifier predictions. Due to properties of ...

Prediction and correct voting is critical task in imbalance data multi-class classification. Accuracy and performance of multi-class depends on voting and prediction of new class data. Assigning of new class of imbalance data generate confusion and decrease the accuracy and performance of classifier. Various authors and research modified the multiclass classification approach such as one agains...

In this article, with the aim to avoid the loss of information that occurs in the usual one-versus-one SVM decomposition procedure of the two-phases (decomposition, reconstruction) multi-classification scheme tri-class SVM approach is addressed. As the most relevant result, it will be demonstrated the robustness improvement of the proposed scheme based on tri-class machine versus that based on ...

In this paper, we propose a method of multiple kernel learning (MKL) to inherently deal with multi-class classification problems. The performances of kernel-based classification methods depend on the employed kernel functions, and it is difficult to predefine the optimal kernel. In the framework of MKL, multiple types of kernel functions are linearly integrated with optimizing the weights for t...