Binary Golden Eagle Optimizer with Time-Varying Flight Length for feature selection

Eluri, Rama Krishna; Devarakonda, Nagaraju

doi:10.1016/j.knosys.2022.108771

Cited by 31 publications

(15 citation statements)

References 33 publications

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“…Nevertheless, the features have moved to a new location. Assume that each feature in the dataset was chosen at random [30].…”

Section: Binary Golden Eagle Optimizer For Feature Selectionmentioning

confidence: 99%

Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

Jajam¹,

Challa²

2023

Computers, Materials &Amp; Continua

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In the insurance sector, a massive volume of data is being generated on a daily basis due to a vast client base. Decision makers and business analysts emphasized that attaining new customers is costlier than retaining existing ones. The success of retention initiatives is determined not only by the accuracy of forecasting churners but also by the timing of the forecast. Previous works on churn forecast presented models for anticipating churn quarterly or monthly with an emphasis on customers' static behavior. This paper's objective is to calculate daily churn based on dynamic variations in client behavior. Training excellent models to further identify potential churning customers helps insurance companies make decisions to retain customers while also identifying areas for improvement. Thus, it is possible to identify and analyse clients who are likely to churn, allowing for a reduction in the cost of support and maintenance. Binary Golden Eagle Optimizer (BGEO) is used to select optimal features from the datasets in a preprocessing step. As a result, this research characterized the customer's daily behavior using various models such as RFM (Recency, Frequency, Monetary), Multivariate Time Series (MTS), Statistics-based Model (SM), Survival analysis (SA), Deep learning (DL) based methodologies such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Customized Extreme Learning Machine (CELM) are framed the problem of daily forecasting using this description. It can be concluded that all models produced better overall outcomes with only slight variations in performance measures. The proposed CELM outperforms all other models in terms of accuracy (96.4).

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“…Nevertheless, the features have moved to a new location. Assume that each feature in the dataset was chosen at random [30].…”

Section: Binary Golden Eagle Optimizer For Feature Selectionmentioning

confidence: 99%

Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

Jajam¹,

Challa²

2023

Computers, Materials &Amp; Continua

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“…Metaheuristic algorithms have been successfully used to solve complex engineering and science computation problems, such as function optimization [ 4 , 5 , 6 ], engineering optimization [ 7 , 8 , 9 ], and feature selection problems [ 10 ]. Researchers have proposed binary metaheuristic algorithms or improved versions for feature selection, such as binary swarm optimization (BPSO) [ 11 ], the binary artificial bee colony (BABC) [ 12 ], the binary gravitational search algorithm (BGSA) [ 13 ], binary grey wolf optimization (BGWO) [ 14 ], the binary salp swarm algorithm (BSSA) [ 15 ], the binary bat algorithm (BBA) [ 16 ], the binary whale optimization algorithm (BWOA) [ 17 ], binary spotted hyena optimization (BSHO) [ 18 ], binary emperor penguin optimization (BEPO) [ 19 ], binary Harris hawks optimization (BHHO) [ 20 ], binary equilibrium optimization (BEO) [ 21 ], binary atom search optimization (BASO) [ 22 ], the binary dragonfly algorithm (BDA) [ 23 ], the binary jaya algorithm (BJA) [ 24 ], binary coronavirus herd immunity optimization (BCHIO) [ 25 ], the binary butterfly optimization algorithm (BBOA) [ 26 ], binary black widow optimization (BBWO) [ 27 ], the binary slime mould algorithm (BSMA) [ 28 ], binary golden eagle optimization (BGEO) [ 29 ], and so on.…”

Section: Introductionmentioning

confidence: 99%

BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection

Li,

Luo,

Zhou

2024

Biomimetics

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Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.

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“…Eluri et al 24 introduces a novel wrapper-based method called BGEO-TVFL for addressing feature selection challenges. Their proposed BGEO-TVFL method employs Binary Golden Eagle Optimizer with Time Varying Flight Length (TVFL) to enhance feature selection.…”

Section: Introductionmentioning

confidence: 99%

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

Abdelrazek,

Abd Elaziz,

El-Baz

2024

Sci Rep

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In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions.

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Binary Golden Eagle Optimizer with Time-Varying Flight Length for feature selection

Cited by 31 publications

References 33 publications

Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

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