Journal ArticleDOI
A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm
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TLDR
Two-stage feature selection and feature extraction is used to improve the performance of text categorization and the proposed model is able to achieve high categorization effectiveness as measured by precision, recall and F-measure.Abstract:
Text categorization is widely used when organizing documents in a digital form. Due to the increasing number of documents in digital form, automated text categorization has become more promising in the last ten years. A major problem of text categorization is its large number of features. Most of those are irrelevant noise that can mislead the classifier. Therefore, feature selection is often used in text categorization to reduce the dimensionality of the feature space and to improve performance. In this study, two-stage feature selection and feature extraction is used to improve the performance of text categorization. In the first stage, each term within the document is ranked depending on their importance for classification using the information gain (IG) method. In the second stage, genetic algorithm (GA) and principal component analysis (PCA) feature selection and feature extraction methods are applied separately to the terms which are ranked in decreasing order of importance, and a dimension reduction is carried out. Thereby, during text categorization, terms of less importance are ignored, and feature selection and extraction methods are applied to the terms of highest importance; thus, the computational time and complexity of categorization is reduced. To evaluate the effectiveness of dimension reduction methods on our purposed model, experiments are conducted using the k-nearest neighbour (KNN) and C4.5 decision tree algorithm on Reuters-21,578 and Classic3 datasets collection for text categorization. The experimental results show that the proposed model is able to achieve high categorization effectiveness as measured by precision, recall and F-measure.read more
Citations
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Journal ArticleDOI
An improved sine cosine algorithm to select features for text categorization
TL;DR: A new improved algorithm of the original Sine Cosine Algorithm (SCA) for feature selection, which allows for better exploration in the search space and makes it very useful for text categorization problem.
Journal ArticleDOI
Term weighting scheme for short-text classification: Twitter corpuses
Issa M. Alsmadi,Gan Keng Hoon +1 more
TL;DR: A simple supervised term weighting approach, which considers the special nature of short texts based on term strength and term distribution, is introduced and its effect in a high-dimensional vector space overterm weighting schemes is assessed.
Journal ArticleDOI
Hybridized term-weighting method for Dark Web classification
TL;DR: A hybridized feature selection method based on the basic term-weighting techniques for accurate terrorism activities detection in textual contexts and results revealed that the classification performance achieved by hybridizing few feature sets is relatively competitive in the number of features used for classification with higher hybridization levels.
Proceedings ArticleDOI
Unsupervised feature selection technique based on genetic algorithm for improving the Text Clustering
TL;DR: The genetic algorithm (GA) is proposed to solve the unsupervised feature selection problem, namely, (FSGATC), which is used to create a new subset of informative features in order to obtain more accurate clusters.
Journal ArticleDOI
Distributed Feature Selection for Efficient Economic Big Data Analysis
TL;DR: A new framework for efficient analysis of high-dimensional economic big data based on innovative distributed feature selection and econometric model construction to reveal the hidden patterns for economic development is presented.
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