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

International Journal of Intelligent Robotics and Applications volume 6, pages 497–521 (2022)Cite this article

Abstract

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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Abbreviations

E-mail:

Electronic mail

GA:

Genetic algorithm

RF:

Random forest

NN:

Neural networks

NSA:

Negative selection algorithm

WHT:

Walsh-Hadamard transform

WOA:

Whale optimization algorithm

GMDH:

Group method of data handling

MLP:

Multi-layer perceptron

SVM:

Support vector machine

PSO:

Particle Swarm Optimization

FDA:

Fisher discriminate analysis

FNR:

False negative rate

Qos:

Quality of service

LOF:

Local outlier factor

NB:

Naïve Bayes

MSSCA:

Multi-Split Spam Corpus Algorithm

FPR:

False-positive rates

DE:

Differential evolution

DT:

Decision tree

MCC:

Mathews correlation coefficient

FDR:

False discovery rate

NPV:

Negative predictive value

MAMH:

MAS as Metaheuristic

BMAMH:

Binary MAMH

TFIDF:

Term frequency inverse document frequency

VSA:

Vortex search algorithm

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Authors and Affiliations

  1. Computer Engineering, Sinhgad Institute of Technology Lonavala, Pune, India

    Kadam Vikas Samarthrao

  2. Electronics and Telecommunication Department, Sinhgad Institute of Technology and Science Narhe, Pune, India

    Vandana M. Rohokale

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  1. Kadam Vikas Samarthrao
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  2. Vandana M. Rohokale
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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). https://doi.org/10.1007/s41315-021-00217-9

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Keywords

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