Archimedes Optimization Algorithm-Based Feature Selection with Hybrid Deep-Learning-Based Churn Prediction in Telecom Industries

Mengash, Hanan Abdullah; Alruwais, Nuha; Kouki, Fadoua; Singla, Chinu; Abd Elhameed, Elmouez Samir; Mahmud, Ahmed

doi:10.3390/biomimetics9010001

Cited by 3 publications

(4 citation statements)

References 36 publications

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“…Fisher score is a statistical measure used in feature selection to evaluate the discriminatory ability of features in distinguishing between different classes in a dataset [ 36 , 37 ]. The equation for Fisher score is given as follows:

where

is the variance among the means of different classes, while

is the average variance within each class.…”

Section: Methodsmentioning

confidence: 99%

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Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Yue,

Hu,

Zhu

2024

Biomimetics

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Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender–emotion model. First, gender and emotion features were extracted from voice signals to lay the foundation for our recognition model. Second, a genetic algorithm (GA) processed high-dimensional features, and the Fisher score was used for evaluation. Third, features were ranked by their importance, and the GA was improved through novel crossover and mutation methods based on feature importance, to improve the recognition accuracy. Finally, the proposed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due to subtle vocal differences, overlapping pitch and tone variability, and similar prosodic features. Notably, the primary features for gender-based differentiation mainly involved mel frequency cepstral coefficients (MFCC) and log MFCC.

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where

is the variance among the means of different classes, while

is the average variance within each class.…”

Section: Methodsmentioning

confidence: 99%

“…Fisher score is a statistical measure used in feature selection to evaluate the discriminatory ability of features in distinguishing between different classes in a dataset [36,37]. The equation for Fisher score is given as follows:…”

Section: Feature Evaluationmentioning

confidence: 99%

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Yue,

Hu,

Zhu

2024

Biomimetics

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show abstract

“…Finally, [56] introduces a technique for predicting customer churn in the telecommunications sector. The Archimedes Optimization Algorithm is employed for optimal feature selection, combined with a hybrid deep learning model for customer churn prediction.…”

Section: Related Workmentioning

confidence: 99%

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Olivares,

Ravelo,

Soto

et al. 2024

Preprint

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Stagnation at local optima represents a significant challenge in bio–inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca Predator Algorithm with Deep Q–Learning. Orca Predator Algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q–Learning is a reinforcement learning technique that combines Q–Learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of Orca Predator Algorithm to identify promising regions nearby in the search space, complemented by the fine–tuning capabilities of Deep Q–Learning to navigate these areas precisely. The practical application of this approach is evaluated using the high–dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of Orca Predator Algorithm against its native version and a set of state–of–the–art algorithms.

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“…Finally, ref. [43] introduces a technique for predicting customer churn in the telecommunications sector. The Archimedes optimization algorithm is employed for optimal feature selection, combined with a hybrid deep learning model for customer churn predic-tion.…”

Section: Related Workmentioning

confidence: 99%

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Olivares,

Ravelo,

Soto

et al. 2024

Mathematics

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Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q-learning is a reinforcement learning technique that combines Q-learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of the Orca predator algorithm to identify promising regions nearby in the search space, complemented by the fine-tuning capabilities of deep Q-learning to navigate these areas precisely. The practical application of this approach is evaluated using the high-dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy’s capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of the Orca predator algorithm against its native version and a set of state-of-the-art algorithms.

show abstract

Archimedes Optimization Algorithm-Based Feature Selection with Hybrid Deep-Learning-Based Churn Prediction in Telecom Industries

Cited by 3 publications

References 36 publications

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

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