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A hybrid meta-heuristic-based multi-objective feature selection with adaptive capsule network for automated email spam detection


Spam emails are not essential because they include dangerous spyware and viruses. Thus, there is an urgent requirement to identify spam emails due to the adaptive nature of unsolicited email. Different approaches are proposed to detect the spam emails, which are developed by considering the machine learning-based algorithms that aim for minimizing the unnecessary emails and obtain outcomes at an accurate rate for the prediction of spam email. These systems focus on solving the issues of different email spam devastating the system. Moreover, the performance of the conventional models is required to be improved, and so this paper implements the email spam detection model for both image and text datasets. Here, the main contribution is considered as the development of multi-objective feature selection and adaptive capsule network for the email spam detection. While using the text datasets, two feature extraction techniques like Term Variance (TV), and Term Frequency-Inverse Document Frequency (TF-IDF) is used, whereas the Fisher Discriminate Analysis (FDA), Walsh-Hadamard Transform (WHT), and color correlogram are used as the feature extraction techniques for handling image datasets. As the length of the features seems to be long, and for reducing the training complexity, the multi-objective feature selection is performed by the hybrid meta-heuristic algorithm Grey-Sail Fish Optimization (G-SFO) algorithm. Further, a novel adaptive Capsule network is used for email spam detection based on the improvement done by the proposed G-SFO algorithm. The efficiency of the suggested model is evaluated with other existing approaches to show better spam detection accuracy.

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Electronic mail


Genetic algorithm


Random forest


Neural networks


Negative selection algorithm


Walsh-Hadamard transform


Whale optimization algorithm


Group method of data handling


Multi-layer perceptron


Support vector machine


Particle Swarm Optimization


Fisher discriminate analysis


False negative rate


Quality of service


Local outlier factor


Naïve Bayes


Multi-Split Spam Corpus Algorithm


False-positive rates


Differential evolution


Decision tree


Mathews correlation coefficient


False discovery rate


Negative predictive value


MAS as Metaheuristic


Binary MAMH


Term frequency inverse document frequency


Vortex search algorithm


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Correspondence to Kadam Vikas Samarthrao.

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Samarthrao, K.V., Rohokale, V.M. A hybrid meta-heuristic-based multi-objective feature selection with adaptive capsule network for automated email spam detection. Int J Intell Robot Appl 6, 497–521 (2022).

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  • Spam email
  • Email spam detection
  • Multi-objective feature selection
  • Adaptive capsule network
  • Grey-Sail Fish Optimization Algorithm