Trends and Patterns of Text Classification Techniques: A Systematic Mapping Study

Maw, Maw; Balakrishnan, Vimala; Rana, Omer F.; Ravana, Sri Devi

doi:10.22452/mjcs.vol33no2.2

Cited by 8 publications

(1 citation statement)

References 41 publications

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“…Alongside the above mentioned studies on ensemble classifiers, a generalized SMS executed in [16] considered 149 publications from the existing literature, which were gathered and examined. Conducting an SMS has also been adopted in various other domains, like fault prediction [17] and text classification [18], to gain deeper insight into the causes and nuances of classification problems. However, as far as is known, SMS has not yet been performed on multi-class imbalanced data classification, underscoring the necessity for an overview mapping review to bridge this gap.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Imbalanced Data Classification: A Systematic Mapping Study

Wang,

Rosli,

Musa

et al. 2024

Eng. Technol. Appl. Sci. Res.

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Multi-class data classification is distinguished as a significant and challenging research topic in contemporary machine learning, particularly when concerning imbalanced data sets. Hence, a thorough investigation of multi-class imbalanced data classification is becoming increasingly pertinent. In this paper, an overview of multi-class imbalanced data classification was generated via conducting a systematic mapping study, which endeavors to analyze the state of contemporary multi-class imbalanced data classification, with the primary goal of ascertaining the corpus of research undertaken in machine learning. To achieve this aim, 7,164 papers were assessed and the 147 prominent ones were selected from five digital libraries, which were further categorized according to techniques, issues, and types of datasets. After a thorough review of these papers, a taxonomy of multi-class imbalanced data classification techniques is proposed. Based on the results, researchers widely employ algorithmic-level, ensemble, and oversampling strategies to address the issue of multi-class imbalance in medical datasets, primarily to mitigate the impact of challenging data factors. This research highlights an urgent need for more studies on multi-class imbalanced data classification.

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Section: Introductionmentioning

confidence: 99%

Multi-Class Imbalanced Data Classification: A Systematic Mapping Study

Wang,

Rosli,

Musa

et al. 2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

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Data Mining Methods for Analysis and Forecast of an Emerging Technology Trend: A Systematic Mapping Study from SCOPUS Papers

Viet

Kravets

Hoang

2021

Lecture Notes in Computer Science

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Text classification using deep learning techniques: a bibliometric analysis and future research directions

Sarin,

Kumar,

Mukund

2023

BIJ

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PurposeText classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.Design/methodology/approachThe authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.FindingsThe study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.Originality/valueThe study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

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Trends and Patterns of Text Classification Techniques: A Systematic Mapping Study

Cited by 8 publications

References 41 publications

Multi-Class Imbalanced Data Classification: A Systematic Mapping Study

Multi-Class Imbalanced Data Classification: A Systematic Mapping Study

Data Mining Methods for Analysis and Forecast of an Emerging Technology Trend: A Systematic Mapping Study from SCOPUS Papers

Text classification using deep learning techniques: a bibliometric analysis and future research directions

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