Filter feature selection methods are utilized to select discriminative terms from high-
dimensional text data to improve text classification performance and reduce computational …

Although the utility of the machine learning (ML) techniques is established in occupational
accident domain using reactive data, its exploration in predicting injury severity using both …

A common challenge in classification and data mining is the class imbalance problem,
where one class in a dataset has significantly more samples than another. The presence of …

This paper presents a semantically rich document representation model for automatically
classifying financial documents into predefined categories utilizing deep learning. The …

Text documents usually contain high dimensional non-discriminative (irrelevant and noisy)
terms which lead to steep computational costs and poor learning performance of text …

The solution of the text classification and time-varying problems are two basic practical
problems frequently encountered in the fields of science and engineering, and most of the …

Abstract Information retrieval has been widely studied due to the growing amounts of textual
information available electronically. Nowadays organizations and industries are facing the …

Learning on noisy datasets is a challenging problem when pre-trained language models are
applied to real-world text classification tasks. In numerous industrial applications, acquiring …

Classifying or predicting occupational incidents using both structured and unstructured (text)
data are an unexplored area of research. Unstructured texts, ie, incident narratives are often …

Optimizing text preprocessing and text classification algorithms is an important, everyday
task in large organizations and companies and it usually involves a labor-intensive and time …