feature selection methods for text classification a systematic literature review

Feature selection methods for text classification

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• Li Q Zhao S He T Wen J (2024) A simple and efficient filter feature selection method via document-term matrix unitization Pattern Recognition Letters 10.1016/j.patrec.2024.02.025 181 (23-29) Online publication date: May-2024 https://doi.org/10.1016/j.patrec.2024.02.025
• Al-Saleh M Alkouz A Alarabeyyat A Bouchahma M (2023) Towards Classifying File Segments in Memory Using Machine-Learning 2023 9th International Conference on Information Technology Trends (ITT) 10.1109/ITT59889.2023.10184243 (44-49) Online publication date: 24-May-2023 https://doi.org/10.1109/ITT59889.2023.10184243
• Ibrahim A Alfonse M Aref M (2023) Effectiveness of Feature Selection in Text Summarization 2023 Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS) 10.1109/ICICIS58388.2023.10391140 (128-133) Online publication date: 21-Nov-2023 https://doi.org/10.1109/ICICIS58388.2023.10391140
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• feature selection
• random sampling
• regularized least squares classification
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• DOI: 10.1007/s11042-018-6083-5
• Corpus ID: 13684595

Feature selection for text classification: A review

• Xuelian Deng , Yuqing Li , +1 author Jilian Zhang
• Published in Multimedia tools and… 8 May 2018
• Computer Science

254 Citations

A novel feature and class-based globalization technique for text classification, a new big data feature selection approach for text classification, a review of semi-supervised learning for text classification, does a hybrid neural network based feature selection model improve text classification.

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Feature selection methods for text classification: a systematic literature review

Tktc: a framework for top-k text classification of multimedia computing in wireless networks, a feature selection method for multi-label text based on feature importance, text classification using naïve bayes classifier, a novel class-center vector model for text classification using dependencies and a semantic dictionary, a new approach for text documents classification with invasive weed optimization and naive bayes classifier, 126 references, a new feature selection based on comprehensive measurement both in inter-category and intra-category for text categorization, an extensive empirical study of feature selection metrics for text classification, feature selection for text classification with naïve bayes, best terms: an efficient feature-selection algorithm for text categorization, feature selection in svm text categorization, ocfs: optimal orthogonal centroid feature selection for text categorization, hybrid feature selection for text classification, feature selection for text categorization on imbalanced data, feature selection for ordinal text classification, some effective techniques for naive bayes text classification, related papers.

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Feature selection methods for text classification: a systematic literature review

• Published: 24 February 2021
• Volume 54 , pages 6149–6200, ( 2021 )

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• Julliano Trindade Pintas   ORCID: orcid.org/0000-0001-5416-8982 1 ,
• Leandro A. F. Fernandes   ORCID: orcid.org/0000-0001-8491-793X 1 &
• Ana Cristina Bicharra Garcia   ORCID: orcid.org/0000-0002-3797-5157 2  

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Feature Selection (FS) methods alleviate key problems in classification procedures as they are used to improve classification accuracy, reduce data dimensionality, and remove irrelevant data. FS methods have received a great deal of attention from the text classification community. However, only a few literature surveys include them focusing on text classification, and the ones available are either a superficial analysis or present a very small set of work in the subject. For this reason, we conducted a Systematic Literature Review (SLR) that asses 1376 unique papers from journals and conferences published in the past eight years (2013–2020). After abstract screening and full-text eligibility analysis, 175 studies were included in our SLR. Our contribution is twofold. We have considered several aspects of each proposed method and mapped them into a new categorization schema. Additionally, we mapped the main characteristics of the experiments, identifying which datasets, languages, machine learning algorithms, and validation methods have been used to evaluate new and existing techniques. By following the SLR protocol, we allow the replication of our revision process and minimize the chances of bias while classifying the included studies. By mapping issues and experiment settings, our SLR helps researchers to develop and position new studies with respect to the existing literature.

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Appendix A. List of acronyms

Accuracy Measure

Balanced Accuracy Measure

At Least One FeaTure

Analysis of Variance

Bit-priori Association Classification Algorithm

Binary Black Hole Algorithm

Blended Feature Selection Method

Binary Gravitational Search Algorithm

Balanced Mutual Information

Bag of Discriminative Words

Bag of Words

Binary Particle Swarm Optimization

Correlative Association Score

Class Discriminating Measure

Comprehensively Measure Feature Selection

Convolutional Neural Network

Crowd-based Feature Selection

Cat Swarm Optimization

Deep Belief Network

Document Frequency

Discriminative Features Selection

Distinguishing Feature Selector

Diversified Greedy Backward-Forward Search

Discriminative Personal Purity

Decision Tree

Ensemble Embedded Feature Selection

Fuzzy Rough Feature Selection

Feature Selection

Genetic Algorithm and Wrapper Approaches

Global Filter-based Feature Selection Scheme

Geometric Particle Swarm Optimization

Hierarchical Attention Network

Hebb Rule Based Feature Selection

Inverse Document Frequency

Information Gain

Improved Particle Swarm Optimization

Improved Sine Cosine Algorithm

k -Nearest Neighbors

Latent Dirichlet Allocation

Latent Selection Augmented Naive Bayes

Markov Blanket Filter

Meta Feature Selection

Memetic Feature Selection based on Label Frequency Difference

Mutual Information

Multivariate Mutual Information

Max-Min Ratio

Multi-Objective Automated Negotiation based Online Feature Selection

Multi-Objective Relative Discriminative Criterion

Multivariate Relative Discrimination Criterion

Naive Bayes

Normalized Difference Measure

Optimized Swarm Search-based Feature Selection

Pairwise Comparison Transformation

Part of Speech

Part of Speech Filter

Particle Swarm Optimization

Reuters Corpus Volume I

Relative Discrimination Criterion

Random Forest

Recursive Feature Elimination

Random Projection and Gram-Schmidt Orthogonalization

Sparsity Adjusted Information Gain

Spark BAT Feature Selection

Square of Information Gain and Chi-square

Systematic Literature Review

Synthetic Minority Oversampling Technique

Support Vector Machines

Support Vector Machine-Recursive Feature Elimination

Small World Algorithm

Student’s t -Test

Term Frequency

Term Frequency-Inverse Document Frequency

Wrapper Feature Selection Algorithm based on Iterated Greedy

Wolf Intelligence Based Optimization of Multi-Dimensional Feature Selection Approach

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Pintas, J.T., Fernandes, L.A.F. & Garcia, A.C.B. Feature selection methods for text classification: a systematic literature review. Artif Intell Rev 54 , 6149–6200 (2021). https://doi.org/10.1007/s10462-021-09970-6

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Published : 24 February 2021

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DOI : https://doi.org/10.1007/s10462-021-09970-6

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