Text categorization based on a new classification by thresholds

Cherif, Walid; Madani, Abdellah; Kissi, Mohamed

doi:10.1007/s13748-021-00247-1

Cited by 5 publications

(2 citation statements)

References 65 publications

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“…In the context of ensemble-based text classification for spam detection is compared with SVM [14], RF [15], NB [16] with several performance metrics can be utilized to evaluate the effectiveness of the approach. These metrics provide insights into the model's accuracy, precision, recall, and its ability to handle different aspects of the classification task.…”

Section: Performance Analysesmentioning

confidence: 99%

Ensemble-Based Text Classification for Spam Detection

Zhang

2024

IJCAI

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This research proposes an ensemble-based approach for spam detection in digital communication, addressing the escalating challenge posed by unsolicited messages, commonly known as spam. The exponential growth of online platforms has necessitated the development of effective information filtering systems to maintain security and efficiency. The proposed approach involves three main components: feature extraction, classifier selection, and decision fusion. The feature extraction techniques are word embedding, are explored to represent text messages effectively. Multiple classifiers, including RNN including LSTM and GRU are evaluated to identify the best performers for spam detection. By employing the ensemble model combines the strengths of individual classifiers to achieve higher accuracy, precision, and recall. The evaluation of the proposed approach utilizes widely accepted metrics on benchmark datasets, ensuring its generalizability and robustness. The experimental results demonstrate that the ensemble-based approach outperforms individual classifiers, offering an efficient solution for combatting spam messages. Integration of this approach into existing spam filtering systems can contribute to improved online communication, user experience, and enhanced cybersecurity, effectively mitigating the impact of spam in the digital landscape.Povzetek: Raziskava uvaja ansambelski pristop za detekcijo spama v digitalni komunikaciji, ki združuje ekstrakcijo značilnosti, izbor klasifikatorjev in fuzijo odločitev za večjo natančnost.

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Section: Performance Analysesmentioning

confidence: 99%

Ensemble-Based Text Classification for Spam Detection

Zhang

2024

IJCAI

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“…The process of text classification is equivalent to the process of set mapping in the mathematical field. In this process, the text sets to be classified of unknown categories are mapped to the corresponding category sets one by one according to the predefined category information [7][8]. The basic framework of text classification is shown in Figure 1: The text classification model can be divided into two processes, one is the training process the other is the classification process.…”

Section: Text Classification Technologymentioning

confidence: 99%

Hybrid Leapfrog Algorithm in Feature Selection Optimization of Text Classification

2022

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The research of hybrid leapfrog algorithm has always been a hot spot. The performance of the classifier model can be improved by improving and optimizing the model. This paper proposes a fusion text semantic structure pattern recognition system based on hybrid leapfrog and support vector machine. The leapfrog algorithm is studied to improve the speed and accuracy of text classification feature selection. This paper mainly uses experimental design and data comparison to illustrate the performance of different algorithms in text classification. The experimental results show that MSFLA-FCM algorithm has great advantages in terms of average fitness value, the maximum fitness reaches 1.518, and the effect of text classification is significantly improved.

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LSTM-SN: complex text classifying with LSTM fusion social network

Wei

Zhang

et al. 2023

J Supercomput

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Text categorization based on a new classification by thresholds

Cited by 5 publications

References 65 publications

Ensemble-Based Text Classification for Spam Detection

Ensemble-Based Text Classification for Spam Detection

Hybrid Leapfrog Algorithm in Feature Selection Optimization of Text Classification

LSTM-SN: complex text classifying with LSTM fusion social network

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