A dual distance metrics method for improving classification performance

A promising classification method for predicting distance students' performance

Predicting the students’ performance is still a challenging task despite being one of the oldest and most popular applications of data mining in education. One of the problems encountered when analyzing data from e-learning platforms is that it presents statistical outliers as a consequence of how students work in online courses. It causes that classifiers are built with less accuracy than desi...

Learning Distance Metrics for Multi-Label Classification

Distance metric learning is a well studied problem in the field of machine learning, where it is typically used to improve the accuracy of instance based learning techniques. In this paper we propose a distance metric learning algorithm that is specialised for multi-label classification tasks, rather than the multiclass setting considered by most work in this area. The method trains an embedder...

A Method for Defuzzication by Weighted Distance

A Method for DMUs Classification in DEA

In data envelopment analysis, anyone can do classification decision units with efficiency scores. It will be interesting if a method for classification of DMUs without regarding to efficiency score is obtained. So in this paper, the classification of Decision Making Units (DMUs) is done according to the additive model without being solved for obtaining scores efficiency. This is because it ...

Evaluation Metrics in Classification: A Quantification of Distance-Bias

This paper provides a characterization of bias for evaluation metrics in classification (e.g., Information Gain, Gini, χ, etc.). Our characterization provides a uniform representation for all traditional evaluation metrics. Such representation leads naturally to a measure for the distance between the bias of two evaluation metrics. We give a practical value to our measure by observing the dista...