Abstract Twin Support Vector Machine (TWSVM) is an emerging machine learning method
suitable for both classification and regression problems. It utilizes the concept of …

Automated classification of text into predefined categories has always been considered as a
vital method to manage and process a vast amount of documents in digital forms that are …

Abstract Least Squares Twin Support Vector Machine (LSTSVM) is a binary classifier and
the extension of it to multiclass is still an ongoing research issue. In this paper, we extended …

In this paper, we propose a filtering method for feature selection called ALOFT (At Least One
FeaTure). The proposed method focuses on specific characteristics of text categorization …

This research is concerned with the improved version of table-based matching algorithm as
the approach to text categorization tasks. It is intended to tackle the three problems in …

The existing information retrieval systems are mostly keyword-based and retrieve relevant
documents or information by matching keywords. Keyword-based search, in spite of its …

In this paper, a Generalized Cluster Centroid based Classifier (GCCC) and its variants for
text categorization are proposed by utilizing a clustering algorithm to integrate two well …

Text categorization is an important research area of text mining. The original purpose of text
categorization is to recognize, understand and organize different types of texts or …

This paper examines the literature on knowledge management (KM) in order to identify the
typical authoring patterns and the focus of the content of published research. The study …

Least squares support vector machine (LS-SVM) for nonlinear regression is sensitive to
outliers in the field of machine learning. Weighted LS-SVM (WLS-SVM) overcomes this …