Preprocessing is one of the key components in a typical text classification framework. This
paper aims to extensively examine the impact of preprocessing on text classification in terms …

Artificial intelligence models are becoming an integral part of modern computing systems.
Just like software inevitably has bugs, models have bugs too, leading to poor classification …

In the feature selection process, reaching the best subset of features is considered a difficult
task. To deal with the complexity associated with this problem, a sophisticated and robust …

In multi-label learning, the training set is made up of instances each associated with a set of
labels, and the task is to predict the label sets of unseen instances. In this paper, this …

High dimensionality of the feature space is one of the most important concerns in text
classification problems due to processing time and accuracy considerations. Selection of …

Feature selection and instance selection are two important data preprocessing steps in data
mining, where the former is aimed at removing some irrelevant and/or redundant features …

Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is
used to simplify and enhance the quality of high-dimensional datasets by selecting …

Sentiment analysis is treated as a classification task as it classifies the orientation of a text
into either positive or negative. This paper describes experimental results that applied …

The feature selection process constitutes a commonly encountered problem of global
combinatorial optimization. This process reduces the number of features by removing …

Building recognition in urban environments aims to identify different buildings in a large-
scale image dataset. This identification facilitates the annotation of any visual object to a …