Feature Selection Techniques for Email Spam Classification: A Survey

  • V. Sri Vinitha
  • D. Karthika Renuka
Conference paper


In this digital world, most of the communication is done only through the Internet. Email is widely used for exchanging information not only for personal communication but also has an important part in business communication because of its effectiveness, fastness, and cost-effective mode of communication. Spam email is the serious problem on the Internet; when users click on to the spam mail, it starts spreading viruses in the user system, consumes lot of network bandwidth and email storage space, and steals user’s confidential data. Feature selection approach selects the best features from the dataset which removes irrelevant, redundant, and noisy data. The proposed paper offers email spam detection which incorporates various feature selection approaches like Information Gain, Correlation-Based Feature Selection, Genetic Algorithm, Ant Colony Optimization, Artificial Bee Colony, Particle Swarm Optimization, Cuckoo Search Algorithm, Harmony Search Algorithm, etc.; when classification is done after feature selection, it will enhance the performance of spam filtering.


Feature selection Information gain Genetic algorithm Artificial bee colony Ant colony optimization Particle swarm optimization Cuckoo search algorithm Harmony search algorithm 



Artificial Bee Colony Optimization




Compact Genetic Algorithm


Evolutionary Algorithm


Firefly-Group Search Optimizer


Hybrid Kernel based Support Vector Machine


Harmony Search Algorithm


K-Nearest Neighbors


Logistic Regression


Multi-Layer Perceptron


Multi-Layer Perceptron Neural Network


Naïve Bayes


Principal Component Analysis


Probabilistic Neural Network




Particle Swarm Optimization


Positive Unlabeled


Random Forests


Stepsize Cuckoo Search


Sequential Minimal Optimization


Support Vector Machine


Term Frequency – Inverse Document Frequency


Text Retrieval Conference


UC Irvine



Our sincere thanks to the University Grants Commission (UGC), Hyderabad, for granting the funds to carry out this work.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • V. Sri Vinitha
    • 1
  • D. Karthika Renuka
    • 2
  1. 1.Bannari Amman Institute of TechnologySathyamangalam, ErodeIndia
  2. 2.PSG College of TechnologyCoimbatoreIndia

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