Multi-label feature selection captures a reliable and informative feature subset from high-
dimensional multi-label data, which plays an important role in pattern recognition. In …

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 …

To date, online review usefulness studies have explored the independent influence of
central and peripheral cues on online review usefulness. Employing the Elaboration …

Text mining is one of the most important tools in Information Retrieval. Text clustering is the
process of classifying documents into predefined categories according to their content …

Multi-label data with high dimensionality, widely existed in the real world, bring many
challenges to the applications of machine learning, pattern recognition and other fields …

Evaluating Preprocessing Techniques in Text Categorization Page 1 International Journal of
Computer Science and Application Issue 2010 ISSN 0974-0767 49 Evaluating Preprocessing …

This paper presents the implementation of a new text document classification framework that
uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean …

This work implements a new text document classifier by integrating the K-nearest neighbor
(KNN) classification approach with the support vector machine (SVM) training algorithm. The …

Based on an effective clustering algorithm-Affinity Propagation (AP)-we present in this paper
a novel semisupervised text clustering algorithm, called Seeds Affinity Propagation (SAP) …

Term weighting schemes often dominate the performance of many classifiers, such as kNN,
centroid-based classifier and SVMs. The widely used term weighting scheme in text …