Interpretable classification of wiki-review streams

Silvia García Méndez, Fátima Leal, Benedita Malheiro, Juan Carlos Burguillo Rial·May 28, 2024

Summary

This research paper presents an interpretable method for real-time classification of reviews in Wikipedia articles to combat vandalism and misinformation. The authors employ stream-based processing, natural language processing, and content-based features, addressing class imbalance with synthetic data generation. The method uses a combination of side (editor properties) and content-derived features, achieving near-90% accuracy on a Wikivoyage dataset. The study highlights the importance of transparency and fairness, with a focus on enhancing data reliability and trust in collaborative platforms by detecting and reverting damaging edits. The research also compares different machine learning models and discusses related work in the context of Wikipedia's collaborative environment.

Key findings

5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of misinformation and reliability in wiki platforms by proposing a transparent and fair method to identify which wiki reviews to revert in real-time . This problem is not new, as wikis, being unmediated collaborative environments, have long suffered from data quality and trustworthiness issues . The proposed solution contributes to real-time transparent identification of deceitful wiki reviews and editors, enhancing the quality and reliability of wiki data .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate a scientific hypothesis related to the classification of wiki-review streams, focusing on the interpretability of the classification models used in the context of online communities and misinformation mitigation . The study aims to address issues such as vandalism detection, revert classification, and the fairness of classification models in the context of wikis, contributing to the transparency, reliability, and real-time processing of data streams in wiki-based platforms .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several innovative ideas, methods, and models in the context of wiki data classification and transparency:

  • Wiki Profiling Methods: The paper introduces various wiki profiling methods, including graph embedding profiling, stylometric profiles, and trust & reputation profiling, to model editors based on their interactions within the platform .
  • Interpretable Classification Models: The paper utilizes Random Forest (RF) classifiers to provide interpretable models that build and explore decision trees, offering explanations through natural language descriptions based on features like ORES article quality probability and edit quality probability .
  • Quality Prediction Models: To address data quality and trustworthiness issues in wikis, the paper employs predictive models to anticipate the quality of reviews, editors, and articles. These models use orthographic similarity, side-based and stylometric profiles, and annotated data sets to predict content reliability using Logistic Regression, Random Forest, and Gradient Boosted Trees .
  • Explainability Efforts: The paper emphasizes the importance of interpretability and explainability in machine learning models. It distinguishes between opaque models (e.g., neural networks) and interpretable models (e.g., decision trees), highlighting the significance of transparent models for enhancing decision-making and responsible machine learning .
  • Model Evaluation Metrics: The proposed method evaluates model performance using standard metrics such as classification accuracy, precision, recall, and F-measure in macro and micro-averaging scenarios. This comprehensive evaluation approach is beneficial for imbalanced classification problems .
  • Explainability Techniques: The paper explores various explainability efforts, including graph-based explanations, model agnostic explanations, word embedding explanations, and visual explanations, to enhance the transparency and interpretability of machine learning models applied to wiki data classification . The paper introduces novel characteristics and advantages compared to previous methods in the context of wiki data classification and transparency :
  • Feature Selection Method: The paper employs a meta-transformer wrapper method for feature selection, utilizing a Random Forest (RF) classifier to establish relative feature importance and reduce the feature space dimension based on importance weights. This method selects features related to editors, ORES probabilities, and content-derived features, enhancing the interpretability of the classification model .
  • Synthetic Data Generation: The proposed synthetic data generation module creates incremental samples of editors' daily activity, particularly focusing on reverted entries to balance the experimental data set. This approach ensures a fully stochastic and multispectral scenario for testing machine learning models, addressing the issue of imbalanced class distribution in the data set .
  • Interpretable Models: The paper emphasizes the use of Random Forest (RF) classifiers, which provide interpretable models by building and exploring decision trees. These decision trees offer transparent explanations of model decisions, detailing the relevant features influencing the classification outcomes in natural language descriptions .
  • Model Evaluation Metrics: The proposed method evaluates model performance using standard metrics such as classification accuracy, precision, recall, and F-measure in macro and micro-averaging scenarios. This comprehensive evaluation approach is crucial for assessing model effectiveness, especially in imbalanced classification problems .
  • Explainability Techniques: The paper leverages interpretable binary classification algorithms like decision rules, decision trees, Naive Bayes, and logistic regression to explain classification outcomes. These self-explainable models offer explicit reasoning behind the classification process, enhancing transparency and interpretability .
  • Data Pre-processing Techniques: The paper employs a three-phase data pre-processing stage involving feature analysis, feature engineering, and feature selection tasks. This approach ensures the identification of relevant features highly correlated with the target variable, leading to valuable data generation for classification and improved model performance .
  • Online Processing Pipeline: The paper transitions from mixed offline and online processing to a fully online processing pipeline combined with hyper-parameter optimization for further enhancement. This shift towards online processing enables real-time modeling and continuous improvement of the classification system .
  • Algorithm for Synthetic Data Generation: The paper contributes an algorithm for generating synthetic data to balance classes, making the final classification fairer. By testing the proposed online method with a real data set from Wikivoyage, balanced through synthetic data, the results achieved near-90% values for all evaluation metrics, ensuring data reliability and fairness .
  • Model Explainability and Transparency: The paper emphasizes the importance of model explainability and transparency in machine learning models applied to wiki data classification. By utilizing interpretable classifiers and providing natural language explanations of model decisions, the paper enhances the interpretability and trustworthiness of the classification outcomes .

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of interpretable classification of wiki-review streams. Noteworthy researchers in this area include Silvia García-Méndez, Fátima Leal, Benedita Malheiro, and Juan Carlos Burguillo-Rial . These researchers have contributed to the development of techniques involving Natural Language Processing and Machine Learning algorithms for analyzing and classifying wiki-review streams.

The key to the solution mentioned in the paper involves the use of an online processing pipeline combined with hyper-parameter optimization for further improvement in the classification of wiki-review streams. Additionally, the proposed method includes the generation of synthetic data for class balancing, which contributes to fairer classification results. The approach was tested with real data from Wikivoyage and achieved near-90% values for all evaluation metrics, including accuracy, precision, recall, and F-measure .


How were the experiments in the paper designed?

The experiments in the paper were designed to compare online and offline performance using balanced data streams. The experiments involved:

  • Offline feature analysis, engineering, and selection.
  • Offline synthetic data generation for class balancing.
  • Incremental profiling, online classification with a balanced data stream, and prediction explanation . The experiments were conducted chronologically, with online models built from scratch and incrementally updated and evaluated, while offline models were trained and tested using distinct data partitions. The classification results were compared between online and offline models based on different data partitions, showcasing the performance of the models .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is based on a well-known set of utilities for extracting and processing MediaWiki data in Python, spanning from 1st January 2004 to 31st December 2019, containing 285,698 samples from 70,260 editors regarding 3,369 different articles . The code for the dataset and related processing tasks is open source and available from the corresponding author upon reasonable request .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study conducted comprehensive experiments comparing online and offline performance with balanced data, utilizing various classification models such as Decision Tree, Random Forest, and Gradient Boosting classifiers . These experiments involved offline feature analysis, engineering, and selection, as well as offline synthetic data generation for class balancing, which contributed to achieving near-90% values for all evaluation metrics .

Furthermore, the study focused on explainability in classification models, particularly highlighting the Random Forest (RF) classifier, which provided the best results and built interpretable decision trees. The explanations generated by the RF classifier covered relevant branches from root to leaf, enhancing the transparency and interpretability of the model . The natural language explanations derived from the RF classifier detailed model decisions based on specific features, aiding in understanding the classification process .

Moreover, the study's methodology involved a combination of offline and online processing, with online models being incrementally updated and evaluated, while offline models were trained and tested using distinct data partitions. The comparison of classification results between online and offline models demonstrated the effectiveness of the online approach, outperforming the offline models in certain metrics, supporting the scientific hypotheses under investigation . The results indicated that the online models showed promising performance, especially in tasks related to revert detection, showcasing the validity of the hypotheses tested in the study .


What are the contributions of this paper?

The contributions of the paper "Interpretable Classification of Wiki-Review Streams" include:

  • Mitigating misinformation in online communities by addressing imbalanced text classification with abstract feature extraction .
  • Addressing the imbalance problem for multi-label classification of scholarly articles .
  • Introducing a method that presents macro and micro class classification metrics near 90% despite the original imbalanced class distribution .
  • Exploring profiling methods in wiki-based platforms, focusing on transparency, fairness, and real-time modeling .
  • Proposing wiki profiling methods such as graph embedding profiling, stylometric profiles, and trust & reputation profiling to model editors and detect vandalism .
  • Comparing online and offline performance with balanced data using various classification algorithms like decision trees, random forests, and gradient boosting classifiers .
  • Building interpretable models with decision trees to explain model decisions based on side and content-derived features for revert and non-revert classifications .
  • Providing natural language explanations based on model decisions for different samples, detailing the features considered and the predicted class .

What work can be continued in depth?

The work on interpretable classification of wiki-review streams can be further extended in several ways:

  • Real-time Identification of Deceitful Wiki Reviews and Editors: The proposed method offers transparent identification of deceptive wiki reviews and editors in real-time, aiding in addressing misinformation and reliability simultaneously .
  • Data Synthetic Generation for Class Balancing: Introducing a data synthetic generation algorithm for class balancing can enhance the fairness of the final classification results. Synthetic data generation has proven to be beneficial in various scenarios, such as testing stochastic scenarios, creating relevant scenarios absent in real data, and automatically labeling entries .
  • Exploration of Self-Explainable Classification Algorithms: The use of self-explainable classification algorithms, like decision trees, enables a clear understanding of why a review has been classified as a revert or a non-revert. This aspect can be further explored to enhance the interpretability of the classification process .

Tables

5

Introduction
Background
[A. Wikipedia's Vandalism Problem]
[B. Importance of Timely Detection]
Objective
[1. To develop an interpretable method]
[2. Combat misinformation and vandalism]
[3. Enhance data reliability and trust]
Method
Data Collection
Stream-Based Processing
[A. Continuous monitoring of Wikipedia articles]
Natural Language Processing
[1. Text preprocessing techniques]
[2. Sentiment analysis and topic extraction]
Content-Based Features
[A. Lexical and semantic features]
[B. N-grams and TF-IDF]
Synthetic Data Generation
[1. Addressing class imbalance]
[2. Data augmentation techniques]
Model Development
Side- and Content-derived Features Integration
[A. Editor properties as features]
[B. Content analysis for classification]
Machine Learning Models
[1. Model Selection]
[A. Comparison of models (e.g., SVM, Random Forest, LSTM)]
[2. Model Performance]
[A. Near-90% accuracy on Wikivoyage dataset]
[3. Model Interpretability]
[A. Transparency in decision-making]
Results and Evaluation
[A. Model performance analysis]
[B. Effectiveness in vandalism detection]
[C. Fairness and transparency evaluation]
Discussion
Related Work
[1. Previous research on Wikipedia vandalism detection]
[2. Collaborative platform analysis]
[3. Implications for community moderation]
Limitations and Future Work
[A. Addressed challenges]
[B. Potential improvements]
[C. Real-time system scalability]
Conclusion
[1. Summary of key findings]
[2. Importance of the proposed method in Wikipedia]
[3. Recommendations for future research]
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What technique does the paper propose for real-time classification of Wikipedia reviews?
What is the accuracy achieved by the method on the Wikivoyage dataset?
How does the study address the issue of class imbalance in the dataset?
What is the primary goal of the research paper discussed?

Interpretable classification of wiki-review streams

Silvia García Méndez, Fátima Leal, Benedita Malheiro, Juan Carlos Burguillo Rial·May 28, 2024

Summary

This research paper presents an interpretable method for real-time classification of reviews in Wikipedia articles to combat vandalism and misinformation. The authors employ stream-based processing, natural language processing, and content-based features, addressing class imbalance with synthetic data generation. The method uses a combination of side (editor properties) and content-derived features, achieving near-90% accuracy on a Wikivoyage dataset. The study highlights the importance of transparency and fairness, with a focus on enhancing data reliability and trust in collaborative platforms by detecting and reverting damaging edits. The research also compares different machine learning models and discusses related work in the context of Wikipedia's collaborative environment.
Mind map
[A. Transparency in decision-making]
[A. Near-90% accuracy on Wikivoyage dataset]
[A. Comparison of models (e.g., SVM, Random Forest, LSTM)]
[2. Sentiment analysis and topic extraction]
[1. Text preprocessing techniques]
[A. Continuous monitoring of Wikipedia articles]
[C. Real-time system scalability]
[B. Potential improvements]
[A. Addressed challenges]
[3. Implications for community moderation]
[2. Collaborative platform analysis]
[1. Previous research on Wikipedia vandalism detection]
[3. Model Interpretability]
[2. Model Performance]
[1. Model Selection]
[B. Content analysis for classification]
[A. Editor properties as features]
[2. Data augmentation techniques]
[1. Addressing class imbalance]
[B. N-grams and TF-IDF]
[A. Lexical and semantic features]
Natural Language Processing
Stream-Based Processing
[3. Enhance data reliability and trust]
[2. Combat misinformation and vandalism]
[1. To develop an interpretable method]
[B. Importance of Timely Detection]
[A. Wikipedia's Vandalism Problem]
[3. Recommendations for future research]
[2. Importance of the proposed method in Wikipedia]
[1. Summary of key findings]
Limitations and Future Work
Related Work
[C. Fairness and transparency evaluation]
[B. Effectiveness in vandalism detection]
[A. Model performance analysis]
Machine Learning Models
Side- and Content-derived Features Integration
Synthetic Data Generation
Content-Based Features
Data Collection
Objective
Background
Conclusion
Discussion
Results and Evaluation
Model Development
Method
Introduction
Outline
Introduction
Background
[A. Wikipedia's Vandalism Problem]
[B. Importance of Timely Detection]
Objective
[1. To develop an interpretable method]
[2. Combat misinformation and vandalism]
[3. Enhance data reliability and trust]
Method
Data Collection
Stream-Based Processing
[A. Continuous monitoring of Wikipedia articles]
Natural Language Processing
[1. Text preprocessing techniques]
[2. Sentiment analysis and topic extraction]
Content-Based Features
[A. Lexical and semantic features]
[B. N-grams and TF-IDF]
Synthetic Data Generation
[1. Addressing class imbalance]
[2. Data augmentation techniques]
Model Development
Side- and Content-derived Features Integration
[A. Editor properties as features]
[B. Content analysis for classification]
Machine Learning Models
[1. Model Selection]
[A. Comparison of models (e.g., SVM, Random Forest, LSTM)]
[2. Model Performance]
[A. Near-90% accuracy on Wikivoyage dataset]
[3. Model Interpretability]
[A. Transparency in decision-making]
Results and Evaluation
[A. Model performance analysis]
[B. Effectiveness in vandalism detection]
[C. Fairness and transparency evaluation]
Discussion
Related Work
[1. Previous research on Wikipedia vandalism detection]
[2. Collaborative platform analysis]
[3. Implications for community moderation]
Limitations and Future Work
[A. Addressed challenges]
[B. Potential improvements]
[C. Real-time system scalability]
Conclusion
[1. Summary of key findings]
[2. Importance of the proposed method in Wikipedia]
[3. Recommendations for future research]
Key findings
5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of misinformation and reliability in wiki platforms by proposing a transparent and fair method to identify which wiki reviews to revert in real-time . This problem is not new, as wikis, being unmediated collaborative environments, have long suffered from data quality and trustworthiness issues . The proposed solution contributes to real-time transparent identification of deceitful wiki reviews and editors, enhancing the quality and reliability of wiki data .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate a scientific hypothesis related to the classification of wiki-review streams, focusing on the interpretability of the classification models used in the context of online communities and misinformation mitigation . The study aims to address issues such as vandalism detection, revert classification, and the fairness of classification models in the context of wikis, contributing to the transparency, reliability, and real-time processing of data streams in wiki-based platforms .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several innovative ideas, methods, and models in the context of wiki data classification and transparency:

  • Wiki Profiling Methods: The paper introduces various wiki profiling methods, including graph embedding profiling, stylometric profiles, and trust & reputation profiling, to model editors based on their interactions within the platform .
  • Interpretable Classification Models: The paper utilizes Random Forest (RF) classifiers to provide interpretable models that build and explore decision trees, offering explanations through natural language descriptions based on features like ORES article quality probability and edit quality probability .
  • Quality Prediction Models: To address data quality and trustworthiness issues in wikis, the paper employs predictive models to anticipate the quality of reviews, editors, and articles. These models use orthographic similarity, side-based and stylometric profiles, and annotated data sets to predict content reliability using Logistic Regression, Random Forest, and Gradient Boosted Trees .
  • Explainability Efforts: The paper emphasizes the importance of interpretability and explainability in machine learning models. It distinguishes between opaque models (e.g., neural networks) and interpretable models (e.g., decision trees), highlighting the significance of transparent models for enhancing decision-making and responsible machine learning .
  • Model Evaluation Metrics: The proposed method evaluates model performance using standard metrics such as classification accuracy, precision, recall, and F-measure in macro and micro-averaging scenarios. This comprehensive evaluation approach is beneficial for imbalanced classification problems .
  • Explainability Techniques: The paper explores various explainability efforts, including graph-based explanations, model agnostic explanations, word embedding explanations, and visual explanations, to enhance the transparency and interpretability of machine learning models applied to wiki data classification . The paper introduces novel characteristics and advantages compared to previous methods in the context of wiki data classification and transparency :
  • Feature Selection Method: The paper employs a meta-transformer wrapper method for feature selection, utilizing a Random Forest (RF) classifier to establish relative feature importance and reduce the feature space dimension based on importance weights. This method selects features related to editors, ORES probabilities, and content-derived features, enhancing the interpretability of the classification model .
  • Synthetic Data Generation: The proposed synthetic data generation module creates incremental samples of editors' daily activity, particularly focusing on reverted entries to balance the experimental data set. This approach ensures a fully stochastic and multispectral scenario for testing machine learning models, addressing the issue of imbalanced class distribution in the data set .
  • Interpretable Models: The paper emphasizes the use of Random Forest (RF) classifiers, which provide interpretable models by building and exploring decision trees. These decision trees offer transparent explanations of model decisions, detailing the relevant features influencing the classification outcomes in natural language descriptions .
  • Model Evaluation Metrics: The proposed method evaluates model performance using standard metrics such as classification accuracy, precision, recall, and F-measure in macro and micro-averaging scenarios. This comprehensive evaluation approach is crucial for assessing model effectiveness, especially in imbalanced classification problems .
  • Explainability Techniques: The paper leverages interpretable binary classification algorithms like decision rules, decision trees, Naive Bayes, and logistic regression to explain classification outcomes. These self-explainable models offer explicit reasoning behind the classification process, enhancing transparency and interpretability .
  • Data Pre-processing Techniques: The paper employs a three-phase data pre-processing stage involving feature analysis, feature engineering, and feature selection tasks. This approach ensures the identification of relevant features highly correlated with the target variable, leading to valuable data generation for classification and improved model performance .
  • Online Processing Pipeline: The paper transitions from mixed offline and online processing to a fully online processing pipeline combined with hyper-parameter optimization for further enhancement. This shift towards online processing enables real-time modeling and continuous improvement of the classification system .
  • Algorithm for Synthetic Data Generation: The paper contributes an algorithm for generating synthetic data to balance classes, making the final classification fairer. By testing the proposed online method with a real data set from Wikivoyage, balanced through synthetic data, the results achieved near-90% values for all evaluation metrics, ensuring data reliability and fairness .
  • Model Explainability and Transparency: The paper emphasizes the importance of model explainability and transparency in machine learning models applied to wiki data classification. By utilizing interpretable classifiers and providing natural language explanations of model decisions, the paper enhances the interpretability and trustworthiness of the classification outcomes .

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of interpretable classification of wiki-review streams. Noteworthy researchers in this area include Silvia García-Méndez, Fátima Leal, Benedita Malheiro, and Juan Carlos Burguillo-Rial . These researchers have contributed to the development of techniques involving Natural Language Processing and Machine Learning algorithms for analyzing and classifying wiki-review streams.

The key to the solution mentioned in the paper involves the use of an online processing pipeline combined with hyper-parameter optimization for further improvement in the classification of wiki-review streams. Additionally, the proposed method includes the generation of synthetic data for class balancing, which contributes to fairer classification results. The approach was tested with real data from Wikivoyage and achieved near-90% values for all evaluation metrics, including accuracy, precision, recall, and F-measure .


How were the experiments in the paper designed?

The experiments in the paper were designed to compare online and offline performance using balanced data streams. The experiments involved:

  • Offline feature analysis, engineering, and selection.
  • Offline synthetic data generation for class balancing.
  • Incremental profiling, online classification with a balanced data stream, and prediction explanation . The experiments were conducted chronologically, with online models built from scratch and incrementally updated and evaluated, while offline models were trained and tested using distinct data partitions. The classification results were compared between online and offline models based on different data partitions, showcasing the performance of the models .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is based on a well-known set of utilities for extracting and processing MediaWiki data in Python, spanning from 1st January 2004 to 31st December 2019, containing 285,698 samples from 70,260 editors regarding 3,369 different articles . The code for the dataset and related processing tasks is open source and available from the corresponding author upon reasonable request .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study conducted comprehensive experiments comparing online and offline performance with balanced data, utilizing various classification models such as Decision Tree, Random Forest, and Gradient Boosting classifiers . These experiments involved offline feature analysis, engineering, and selection, as well as offline synthetic data generation for class balancing, which contributed to achieving near-90% values for all evaluation metrics .

Furthermore, the study focused on explainability in classification models, particularly highlighting the Random Forest (RF) classifier, which provided the best results and built interpretable decision trees. The explanations generated by the RF classifier covered relevant branches from root to leaf, enhancing the transparency and interpretability of the model . The natural language explanations derived from the RF classifier detailed model decisions based on specific features, aiding in understanding the classification process .

Moreover, the study's methodology involved a combination of offline and online processing, with online models being incrementally updated and evaluated, while offline models were trained and tested using distinct data partitions. The comparison of classification results between online and offline models demonstrated the effectiveness of the online approach, outperforming the offline models in certain metrics, supporting the scientific hypotheses under investigation . The results indicated that the online models showed promising performance, especially in tasks related to revert detection, showcasing the validity of the hypotheses tested in the study .


What are the contributions of this paper?

The contributions of the paper "Interpretable Classification of Wiki-Review Streams" include:

  • Mitigating misinformation in online communities by addressing imbalanced text classification with abstract feature extraction .
  • Addressing the imbalance problem for multi-label classification of scholarly articles .
  • Introducing a method that presents macro and micro class classification metrics near 90% despite the original imbalanced class distribution .
  • Exploring profiling methods in wiki-based platforms, focusing on transparency, fairness, and real-time modeling .
  • Proposing wiki profiling methods such as graph embedding profiling, stylometric profiles, and trust & reputation profiling to model editors and detect vandalism .
  • Comparing online and offline performance with balanced data using various classification algorithms like decision trees, random forests, and gradient boosting classifiers .
  • Building interpretable models with decision trees to explain model decisions based on side and content-derived features for revert and non-revert classifications .
  • Providing natural language explanations based on model decisions for different samples, detailing the features considered and the predicted class .

What work can be continued in depth?

The work on interpretable classification of wiki-review streams can be further extended in several ways:

  • Real-time Identification of Deceitful Wiki Reviews and Editors: The proposed method offers transparent identification of deceptive wiki reviews and editors in real-time, aiding in addressing misinformation and reliability simultaneously .
  • Data Synthetic Generation for Class Balancing: Introducing a data synthetic generation algorithm for class balancing can enhance the fairness of the final classification results. Synthetic data generation has proven to be beneficial in various scenarios, such as testing stochastic scenarios, creating relevant scenarios absent in real data, and automatically labeling entries .
  • Exploration of Self-Explainable Classification Algorithms: The use of self-explainable classification algorithms, like decision trees, enables a clear understanding of why a review has been classified as a revert or a non-revert. This aspect can be further explored to enhance the interpretability of the classification process .
Tables
5
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