scholarly journals Whale optimization algorithm-based email spam feature selection method using rotation forest algorithm for classification

2019 ◽  
Vol 1 (5) ◽  
Author(s):  
Maryam Shuaib ◽  
Shafi’i Muhammad Abdulhamid ◽  
Olawale Surajudeen Adebayo ◽  
Oluwafemi Osho ◽  
Ismaila Idris ◽  
...  
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Whale Optimization Algorithm ◽  
Rotation Forest ◽  
Whale Optimization ◽  
Email Spam

scholarly journals A Combination Approach of Two Metaheuristic Algorithm for Optimal Feature Selection: Case Study Email Spam Detection

2020 ◽  
Author(s):  
Hekmat Mohmmadzadeh
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Selection Problem ◽  
Second Step ◽  
Feature Selection Problem ◽  
Flower Pollination ◽  
Opposition Based Learning ◽  
Whale Optimization

Selecting a feature in data mining is one of the most challenging and important activities in pattern recognition. The issue of feature selection is to find the most important subset of the main features in a specific domain, the main purpose of which is to remove additional or unrelated features and ultimately improve the accuracy of the categorization algorithms. As a result, the issue of feature selection can be considered as an optimization problem and to solve it, meta-innovative algorithms can be used. In this paper, a new hybrid model with a combination of whale optimization algorithms and flower pollination algorithms is presented to address the problem of feature selection based on the concept of opposition-based learning. In the proposed method, we tried to solve the problem of optimization of feature selection by using natural processes of whale optimization and flower pollination algorithms, and on the other hand, we used opposition-based learning method to ensure the convergence speed and accuracy of the proposed algorithm. In fact, in the proposed method, the whale optimization algorithm uses the bait siege process, bubble attack method and bait search, creates solutions in its search space and tries to improve the solutions to the feature selection problem, and along with this algorithm, Flower pollination algorithm with two national and local search processes improves the solution of the problem selection feature in contrasting solutions with the whale optimization algorithm. In fact, we used both search space solutions and contrasting search space solutions, all possible solutions to the feature selection problem. To evaluate the performance of the proposed algorithm, experiments are performed in two stages. In the first phase, experiments were performed on 10 sets of data selection features from the UCI data repository. In the second step, we tried to test the performance of the proposed algorithm by detecting spam emails. The results obtained from the first step show that the proposed algorithm, by running on 10 UCI data sets, has been able to be more successful in terms of average selection size and classification accuracy than other basic meta-heuristic algorithms. Also, the results obtained from the second step show that the proposed algorithm has been able to perform spam emails more accurately than other similar algorithms in terms of accuracy by detecting spam emails.

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