# Swarm Intelligence Algorithms for Feature Selection: A Review
## 7 Discussion
Their approach increased the classification accuracy successfully, and decreased the number of selected features compared to other SI methods. There are cases where the nature of the application requires very fast FS algorithms, such as data mining of data streams on the fly. An example of how to improve the speed of an SI based FS algorithm is presented in [77]. The authors proposed an accelerated PSO swarm search FS by simplifying some formulas for updating particles and applying the swarm search approach in incremental manner, obtaining a higher gain in accuracy per second incurred in the pre-processing. Authors in [13] exposed that PSO has similar advantages as genetic algorithms in terms of a straightforward representation. However, neither of them can be used for feature construction. Moreover, scalability might be a bottleneck for both of them, especially when we deal with problems with thousands or more features [75]. Interestingly, we can support this claim by our study too, since nowadays, in the fields such as genetics and bioinformatics, the number of features may exceed a million. For such large dimensions, we need to use very specific algorithms as well as hardware. However, our study has not revealed any special solutions that apply feature selection methods using swarm intelligence on such dimensions. By the same token, no special datasets were found during our literature review. \begin{table}
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**Algorithm** & **Presentation** & **Definition of Step Condition** & **Evaluation** & **Move** & **Validation** & **Advantages** \\ \hline ABC [38] & binary; forward search strategy & 100 iterations & acc (SVM) & with the pertubation frequency & 10-fold CV & + outperforms PSO, ACO, and GA + on average smaller number of selected features \\ \hline ABC [54] & binary; random & 50 iterations & classification error rate (NN) & use of mutation, recombination and selection & 70\% training set, 30\% test set, 10-fold CV on training set & + introduced DE based neighborhood mechanism for FS + method removes redundant features effectively and consistent \\ \hline ACO [35] & graph & 50 iterations & acc (1 NN) & graph search with the probabilistic transition rule & 60\% training set, 40\% test set set & + method permits ants to explore all of the unvisited features simultaneously \\ \hline PSO [75] & continuous; random & different tests & combined function with accordance (FNN) and number of selected features & based on inertia weight, social and cognitive component & different CV techniques & + high classification accuracy, small number of selected features, and lower computing time \\ \hline GWO [92] & binary; random & 100 iterations & combined function with ac(KELM) and number of selected features & based on alpha, beta, and delta wolves & 10-fold CV & + method adaptively converges more quickly, produces much better solution quality, but also gains less number of selected features, achieving high classification performance \\ \hline WOA [67] & binary; random & 100 iterations & combined function with acc (NN) and number of selected features & based on neighbours selected on neighbours & CV & + method combined with the Simulated annealing following two hybrid models: LTH and HRH \\ \hline APSA [52] & binary; random &
\begin{tabular}{} \end{tabular} & acc (SVM) & execution of the three search steps (follow, swarm, and prey) & 10-fold CV & + experiment with and without parameter optimization \\ \hline SPLA [45] & binary; random with two equations & 50 iterations & combined function with acc (1 NN) and number of selected features & with equations containing adaptive transition factor & 10-fold CV & + provided three improvement strategies \\ \hline KH [68] & binary; random & 100 iterations & acc (OPF) & with Sigmoid function & 5-fold CV & + fast method \\ \hline \hline \end{tabular}
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Table 5: Summary of selected algorithms and their components sorted by proposed taxonomy.