Many machine learning algorithms have been applied to text classification tasks. In the machine learning paradigm, a general inductive process automatically builds a text classifier by learning, generally known as supervised learning. However, the supervised learning approaches have some problems. The most notable problem is that they require a large number of labeled training documents for acc...

Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...

Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...

In manipulating data such as in supervised or unsupervised learning, we need to extract new features from the original features for the purpose of reducing the dimension of feature space and achieving better performance. In this paper, we investigate a novel schema for unsupervised feature extraction for classification problems. We based our method on clustering to achieve feature extraction. A...

There are many challenges in mining data streams, such as infinite length, evolving nature and lack of labeled instances. Accordingly, a semi-supervised ensemble approach for mining data streams is presented in this paper. Data streams are divided into data chunks to deal with the infinite length. An ensemble classification model E is trained with existing labeled data chunks and decision bound...

Neural networks are among the most accurate supervised learning methods in use today, but their opacity makes them difficult to trust in critical applications, especially when conditions in training differ from those in test. Recent work on explanations for black-box models has produced tools (e.g. LIME) to show the implicit rules behind predictions, which can help us identify when models are r...

Current transformer (CT) saturation is one of the significant problems for protection engineers. If CT saturation is not tackled properly, it can cause a disastrous effect on the stability of the power system, and may even create a complete blackout. To cope with CT saturation properly, an accurate detection or classification should be preceded. Recently, deep learning (DL) methods have brought...

Machine learning and machine classification have traditionally been done by three different methods. The first method is an unsupervised method, which takes no preclassification of any of the data in order for the data to be grouped together for analysis afterwards. This method then requires a domain specialist to examine each group after the results have been run to label the resulting groups ...