Development of Proposed Ensemble Model for Spam e-mail Classification

Shrivas, Akhilesh Kumar; Dewangan, Amit Kumar; Ghosh, Shameek; Singh, Devendra P.

doi:10.5755/j01.itc.50.3.27349

“…En cuanto a la predicción y consenso del etiquetado considerando la variedad de clasificadores débiles, se encuentran estudios que buscan la automatización de este proceso ajustando el consenso de predicción [42] [43] [44] [22]. Ante las propuestas de estos estudios, uno de las mayores limitantes que se identifica es el alto costo computacional necesario para el procesamiento de las robustas estructuras [37] [38] [40] [42] [43] [39] [22]. Así también se aprecia la pérdida de características cuando el modelo incorpora pre-entrenamiento externo o de otro lenguaje [41].…”

Section: Ensambladounclassified

“…Las métricas de rendimiento de clasificación de estos modelos determinan una vez más que el número de documentos etiquetados es importante para su precisión, los modelos ensamblados más eficientes son [43] 97.25 %, [39] 92.9 %, [44] 88.69 % y efectivamente son los modelos que mayor número de etiquetados disponen, con 30 %, 47 % y 65 % respectivamente del total de documentos. Otros factores que influyen en la eficiencia de estos modelos son su cantidad (5) de clasificadores débiles [42] [43] [39], su entrenamiento por capas (cross-validation) [42], la apertura a documentos pre-entrenados [39] y su eficiencia en el manejo del consenso para la predicción [42] [43]. Mientras que las características de los menos eficientes [40] y [41] recaen en menor cantidad de clasificadores débiles (2), menor cantidad de etiquetados y modelos que en su estructura consideran conjuntos de documentos con diversos lenguajes para su entrenamiento que deterioran la precisión.…”

Section: Ensambladounclassified

Semi-supervised learning models for document classification: A systematic review and meta-analysis

Cevallos-Culqui,

Pons,

Rodriguez

2023

ia

0

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The continuous increase of digital documents on the web creates the need to search for information patterns that allow the categorization of organizational documents to generate knowledge in an institution. An Artificial Intelligence technique for this purpose is text classification, it for its application uses labels (previously categorized documents) with supervised (with labels) or unsupervised (without labels) training models. Both traditional models with their advantages and disadvantages have been joined into semi-supervised models that extract the best qualities of each one, however, the labeling process involves resources and time that try to be optimized to improve classification accuracy. An analysis of the different semi-supervised models would show us the advantages of their training and the way how the structure of each of them affects the accuracy of their classification. In the present study, a classification structure of the semi-supervised models in the classification of documents is proposed to analyze their qualities and categorization process, through an SLR (Revision of systematic literature) that extracts performance metrics from the identified studies to perform a meta-analysis through forest plots. To define the search strategy for studies, the PICOC (Population, Intervention, Comparison, Outputs, Context) method has been used, it is supported by the research question defines a search string, which has allowed the collection of 228 research, these are filtered with the PRISMA declaration method and the determination of exclusion criteria, in this way 35 researches are selected for the present study. The analysis of the selected studies identifies a structure for the different semi-supervised learning models, and a scheme of their work process is obtained, it has been used to extract advantages, disadvantages, and performance metrics. Through a meta-analysis with forest diagrams, the classification accuracy performance of the researches in each learning model is evaluated, determining as results that regardless of the characteristics of its process, active learning (0.89) and assembled learning (0.83) present the best performance levels.

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“…En cuanto a la predicción y consenso del etiquetado considerando la variedad de clasificadores débiles, se encuentran estudios que buscan la automatización de este proceso ajustando el consenso de predicción [42] [43] [44] [22]. Ante las propuestas de estos estudios, uno de las mayores limitantes que se identifica es el alto costo computacional necesario para el procesamiento de las robustas estructuras [37] [38] [40] [42] [43] [39] [22]. Así también se aprecia la pérdida de características cuando el modelo incorpora pre-entrenamiento externo o de otro lenguaje [41].…”

Section: Ensambladounclassified

“…Las métricas de rendimiento de clasificación de estos modelos determinan una vez más que el número de documentos etiquetados es importante para su precisión, los modelos ensamblados más eficientes son [43] 97.25 %, [39] 92.9 %, [44] 88.69 % y efectivamente son los modelos que mayor número de etiquetados disponen, con 30 %, 47 % y 65 % respectivamente del total de documentos. Otros factores que influyen en la eficiencia de estos modelos son su cantidad (5) de clasificadores débiles [42] [43] [39], su entrenamiento por capas (cross-validation) [42], la apertura a documentos pre-entrenados [39] y su eficiencia en el manejo del consenso para la predicción [42] [43]. Mientras que las características de los menos eficientes [40] y [41] recaen en menor cantidad de clasificadores débiles (2), menor cantidad de etiquetados y modelos que en su estructura consideran conjuntos de documentos con diversos lenguajes para su entrenamiento que deterioran la precisión.…”

Section: Ensambladounclassified

Semi-supervised learning models for document classification: A systematic review and meta-analysis

Cevallos-Culqui,

Pons,

Rodriguez

2023

ia

0

View full text Add to dashboard Cite

The continuous increase of digital documents on the web creates the need to search for information patterns that allow the categorization of organizational documents to generate knowledge in an institution. An Artificial Intelligence technique for this purpose is text classification, it for its application uses labels (previously categorized documents) with supervised (with labels) or unsupervised (without labels) training models. Both traditional models with their advantages and disadvantages have been joined into semi-supervised models that extract the best qualities of each one, however, the labeling process involves resources and time that try to be optimized to improve classification accuracy. An analysis of the different semi-supervised models would show us the advantages of their training and the way how the structure of each of them affects the accuracy of their classification. In the present study, a classification structure of the semi-supervised models in the classification of documents is proposed to analyze their qualities and categorization process, through an SLR (Revision of systematic literature) that extracts performance metrics from the identified studies to perform a meta-analysis through forest plots. To define the search strategy for studies, the PICOC (Population, Intervention, Comparison, Outputs, Context) method has been used, it is supported by the research question defines a search string, which has allowed the collection of 228 research, these are filtered with the PRISMA declaration method and the determination of exclusion criteria, in this way 35 researches are selected for the present study. The analysis of the selected studies identifies a structure for the different semi-supervised learning models, and a scheme of their work process is obtained, it has been used to extract advantages, disadvantages, and performance metrics. Through a meta-analysis with forest diagrams, the classification accuracy performance of the researches in each learning model is evaluated, determining as results that regardless of the characteristics of its process, active learning (0.89) and assembled learning (0.83) present the best performance levels.

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“…TC is a machine learning challenge that tries to classify new written content into a conceptual group from a predetermined classification collection [1]. It is crucial in a variety of applications, including sentiment analysis [2,3], spam email filtering [4,5], hate speech detection [6], text summarization [7], website classification [8], authorship attribution [9], information retrieval [10], medical diagnostics [11], emotion detection on smart phones [12], online recommendations [13], fake news detection [14,15], crypto-ransomware early detection [16], semantic similarity detection [17], part-of-speech tagging [18], news classification [19], and tweet classification [20].…”

Section: Introductionmentioning

confidence: 99%

A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

Alhaj

¹

,

Dahou

²

,

Al-qaness

³

et al. 2022

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We propose a novel text classification model, which aims to improve the performance of Arabic text classification using machine learning techniques. One of the effective solutions in Arabic text classification is to find the suitable feature selection method with an optimal number of features alongside the classifier. Although several text classification methods have been proposed for the Arabic language using different techniques, such as feature selection methods, an ensemble of classifiers, and discriminative features, choosing the optimal method becomes an NP-hard problem considering the huge search space. Therefore, we propose a method, called Optimal Configuration Determination for Arabic text Classification (OCATC), which utilized the Particle Swarm Optimization (PSO) algorithm to find the optimal solution (configuration) from this space. The proposed OCATC method extracts and converts the features from the textual documents into a numerical vector using the Term Frequency-Inverse Document Frequency (TF–IDF) approach. Finally, the PSO selects the best architecture from a set of classifiers to feature selection methods with an optimal number of features. Extensive experiments were carried out to evaluate the performance of the OCATC method using six datasets, including five publicly available datasets and our proposed dataset. The results obtained demonstrate the superiority of OCATC over individual classifiers and other state-of-the-art methods.

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Development of Proposed Ensemble Model for Spam e-mail Classification

Cited by 10 publications

References 32 publications

Semi-supervised learning models for document classification: A systematic review and meta-analysis

Semi-supervised learning models for document classification: A systematic review and meta-analysis

Semi-supervised learning models for document classification: A systematic review and meta-analysis

A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

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