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Multi-view learning is a machine learning app0roach aiming to exploit the knowledge retrieved from data, represented by multiple feature subsets known as views. Co-training is considered the most representative form of multi-view learning, a very effective semi-supervised classification algorithm for building highly accurate and robust predictive models. Even though it has been implemented in various scientific fields, it has not adequately used in educational data mining and learning analytics, since the hypothesis about the existence of two feature views cannot be easily implemented. Some notable studies have emerged recently dealing with semi-supervised classification tasks, such as student performance or student dropout prediction, while semi-supervised regression is uncharted territory. Therefore, the present study attempts to implement a semi-regression algorithm for predicting the grades of undergraduate students in the final exams of a one-year online course, which exploits three independent and naturally formed feature views, since they are derived from different sources. Moreover, we examine a well-established framework for interpreting the acquired results regarding their contribution to the final outcome per student/instance. To this purpose, a plethora of experiments is conducted based on data offered by the Hellenic Open University and representative machine learning algorithms. The experimental results demonstrate that the early prognosis of students at risk of failure can be accurately achieved compared to supervised models, even for a small amount of initially collected data from the first two semesters. The robustness of the applying semi-supervised regression scheme along with supervised learners and the investigation of features’ reasoning could highly benefit the educational domain.

In many real world classification tasks, all data classes are not represented equally. This problem, known also as the curse of class imbalanced in data sets, has a potential impact in the training procedure of a classifier by learning a model that will be biased in favor of the majority class. In this work at hand, an under-sampling approach is proposed, which leverages the usage of a Naive Bayes classifier, in order to select the most informative instances from the available training set, based on a random initial selection. The method starts by learning a Naive Bayes classification model on a small stratified initial training set. Afterwards, it iteratively teaches its base model with the instances that the model is most uncertain about and retrains it until some criteria are satisfied. The overall performance of the proposed method has been scrutinized through a rigorous experimental procedure, being tested using six multimodal data sets, as well as another forty-four standard benchmark data sets. The empirical results indicate that the proposed undersampling method achieves comparable classification performance in contrast to other resampling techniques, regarding several proper metrics and having performed a suitable statistical testing procedure.

Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms. This phenomenon is primarily fostered by offensive comments, either during user interaction or in the form of a posted multimedia context. Nowadays, giant corporations own platforms where millions of users log in every day, and protection from exposure to similar phenomena appears to be necessary to comply with the corresponding legislation and maintain a high level of service quality. A robust and reliable system for detecting and preventing the uploading of relevant content will have a significant impact on our digitally interconnected society. Several aspects of our daily lives are undeniably linked to our social profiles, making us vulnerable to abusive behaviours. As a result, the lack of accurate hate speech detection mechanisms would severely degrade the overall user experience, although its erroneous operation would pose many ethical concerns. In this paper, we present ‘ETHOS’ (multi-labEl haTe speecH detectiOn dataSet), a textual dataset with two variants: binary and multi-label, based on YouTube and Reddit comments validated using the Figure-Eight crowdsourcing platform. Furthermore, we present the annotation protocol used to create this dataset: an active sampling procedure for balancing our data in relation to the various aspects defined. Our key assumption is that, even gaining a small amount of labelled data from such a time-consuming process, we can guarantee hate speech occurrences in the examined material.

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 absence of automated mechanisms that produce labeled data and the high cost of needed human effort for completing the procedure of labelization in several scientific domains rise the need for semisupervised methods which counterbalance this phenomenon. In this work, a self-trained Logistic Model Trees (LMT) algorithm is presented, which combines the characteristics of Logistic Trees under the scenario of poor available labeled data. We performed an in depth comparison with other well-known semisupervised classification methods on standard benchmark datasets and we finally reached to the point that the presented technique had better accuracy in most cases.

In recent years, a forward-looking subfield of machine learning has emerged with important applications in a variety of scientific fields. Semi-supervised learning is increasingly being recognized as a burgeoning area embracing a plethora of efficient methods and algorithms seeking to exploit a small pool of labeled examples together with a large pool of unlabeled ones in the most efficient way. Co-training is a representative semi-supervised classification algorithm originally based on the assumption that each example can be described by two distinct feature sets, usually referred to as views. Since such an assumption can hardly be met in real world problems, several variants of the co-training algorithm have been proposed dealing with the absence or existence of a naturally two-view feature split. In this context, a Static Selection Ensemble-based co-training scheme operating under a random feature split strategy is outlined regarding binary classification problems, where the type of the base ensemble learner is a soft-Voting one composed of two participants. Ensemble methods are commonly used to boost the predictive performance of learning models by using a set of different classifiers, while the Static Ensemble Selection approach seeks to find the most suitable structure of ensemble classifier based on a specific criterion through a pool of candidate classifiers. The efficacy of the proposed scheme is verified through several experiments on a plethora of benchmark datasets as statistically confirmed by the Friedman Aligned Ranks non-parametric test over the behavior of classification accuracy, F1-score, and Area Under Curve metrics.

One of the major aspects affecting the performance of the classification algorithms is the amount of labeled data which is available during the training phase. It is widely accepted that the labeling procedure of vast amounts of data is both expensive and time-consuming since it requires the employment of human expertise. For a wide variety of scientific fields, unlabeled examples are easy to collect but hard to handle in a useful manner, thus improving the contained information for a subject dataset. In this context, a variety of learning methods have been studied in the literature aiming to efficiently utilize the vast amounts of unlabeled data during the learning process. The most common approaches tackle problems of this kind by individually applying active learning or semi-supervised learning methods. In this work, a combination of active learning and semi-supervised learning methods is proposed, under a common self-training scheme, in order to efficiently utilize the available unlabeled data. The effective and robust metrics of the entropy and the distribution of probabilities of the unlabeled set, to select the most sufficient unlabeled examples for the augmentation of the initial labeled set, are used. The superiority of the proposed scheme is validated by comparing it against the base approaches of supervised, semi-supervised, and active learning in the wide range of fifty-five benchmark datasets.