Big multimedia data is heterogeneous in essence, that is, the data may be a mixture of
video, audio, text, and images. This is due to the prevalence of novel applications in recent …

Automated feature selection is important for text categorization to reduce feature size and to
speed up learning process of classifiers. In this paper, we present a novel and efficient …

Many real-world text classification tasks involve imbalanced training examples. The
strategies proposed to address the imbalanced classification (eg, resampling, instance …

Feature clustering is a powerful method to reduce the dimensionality of feature vectors for
text classification. In this paper, we propose a fuzzy similarity-based self-constructing …

One of the crucial processes in machine learning algorithms to improve the performance as
well as, in some cases, to reduce the computational cost is feature selection. In other words …

Online selection of dynamic features has attracted intensive interest in recent years.
However, existing online feature selection methods evaluate features individually and …

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

Feature selection has attracted significant attention in data mining and machine learning in
the past decades. Many existing feature selection methods eliminate redundancy by …

Feature selection is usually a separate procedure which can not benefit from result of the
data exploration. In this paper, we propose a unsupervised feature selection method which …

The vast subjective texts spreading all over the Internet promoted the demand for text
sentiment classification technology. A well-known fact that often weakens the performance of …