Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection

Abstract

There are various fields are affected by the growth of data dimensionality. The major problems which are resulted from high dimensionality of data including high memory requirements, high computational cost, and low machine learning classifier performance. Therefore, proper selection of relevant features from the set of available features and the removal of irrelevant features will solve these problems. Therefore, to solve the feature selection problem, an improved version of Dragonfly Algorithm (DA) is proposed by combining it with Simulated Annealing (SA), where the improved algorithm named BDA-SA. To solve the local optima problem of DA and enhance its ability in selecting the best subset of features for classification problems, Simulated Annealing (SA) was applied to the best solution found by Binary Dragonfly algorithm in attempt to improve its accuracy. A set of frequently used data sets from UCI repository was utilized to evaluate the performance of the proposed FS approach. Results show that the proposed hybrid approach, named BDA-SA, has superior performance when compared to wrapper-based FS methods including a feature selection method based on the basic version of Binary Dragonfly Algorithm.

Multiclass feature selection with metaheuristic optimization algorithms: a review

“…In future research, this can also be extended to other real-world situations such as the field of technology (spam mail detection, security threat classification) and the industry-customer purchase behavioral prediction, and more, not just to solve medical problems that currently dominate the research endeavors. The future development of heuristic or metaheuristic approaches may tend toward the use of other classifiers popular, creation of more hybrid local search techniques with metaheuristic methods for more accurate prediction as studied in [20,36,60,136], multi-objective binary techniques creation, application to solving medical diagnosis challenges [180], hybridized wrapper-filter approaches are expected to be developed, and more real-world application areas would be embarked upon [184]. models in learning from a meaningful set of data for prediction and solving real-life problems.…”

“…The result showed the superiority of HIDA. Moreover, in 2021, Chantar et al [ 36 ] also proposed an improved version of the DA as they combined simulated annealing (SA) to resolve the local optima challenge of the DA, and they enhanced the ability of the technique to select the best feature subsets for effective classification. The approach utilized a set of frequently used datasets from the UCI repository to test the performance of the approach.…”

A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets

“…Several works exist in the literature that employed the combination of local search strategy with metaheuristic algorithms. Some of them includes 1 , 74 – 78 .…”

Integration of Specific Local Search Methods in Metaheuristic Algorithms for Optimizing the Feature Selection Process: A Survey Authors

“…This class of local search algorithms also employs the exploration and exploitation phase (Al-betar, 2016). However, they operate by improving only a single solution (Chantar et al, 2021) unlike in the populationbased metaheuristic where a population of possible solutions is considered. The two-way local search algorithm begins operation on a single initial solution and gradually evolves it into a better solution using the neighborhood concept (Ghosh et al, 2019).…”

“…Because of the recognized issue with these MAs' exploitation phase, which causes local optima stagnation, the hybridization of these algorithms with specific local search algorithms such as the Simulated Annealing (SA) (Kirkpatrick et al, 1983), the Variable neighborhood search (VNS) (Hansen & Mladenović, 2005) among others have been studied . These local search algorithms are metaheuristics in and of themselves, but they are categorized as a trajectory or local search techniques since they deal with a single solution rather than a population of possible solutions like population-based metaheuristics (Chantar et al, 2021). The main focus of our survey study is on integrating population-based MA with these local search algorithms to solve the FS process.…”