A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

Rojas, Matías Gabriel; Olivera, Ana Carolina; Carballido, Jessica Andrea; Vidal, Pablo Javier

doi:10.1109/tla.2020.9398628

Cited by 9 publications

(5 citation statements)

References 34 publications

(45 reference statements)

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“…An Evolutionary Algorithm (EA) is a computational method that solves problems by mimicking the behavior of living organisms using nature-inspired mechanisms 21 . The use of EAs for feature selection has received significant attention in academia, with various algorithms being proposed, including Particle Swarm Optimization (PSO) 22 – 24 , Genetic Algorithm (GA) 25 , 26 , Artificial Bee Colony (ABC) 27 , Genetic Programming (GP) 28 , Gravitational Search Algorithm (GSA) 29 and Ant Colony Optimization (ACO) 30 , 31 . One advantage of EAs is their population-based search approach, which involves a team of entities exploring the fitness landscape to find the globally optimum solution.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Bird’s Eye View feature selection for high-dimensional data

Belhaouari

Shakeel

Erbad

et al. 2023

Sci Rep

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In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this challenge, the Bird’s Eye View (BEV) feature selection technique is introduced. This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. The proposed strategy in this paper leads to improved classification performance and a reduced number of features compared to conventional methods, as demonstrated by outperforming state-of-the-art feature selection techniques across multiple benchmark datasets.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Bird’s Eye View feature selection for high-dimensional data

Belhaouari

Shakeel

Erbad

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…An Evolutionary Algorithm (EA) is a computational method that solves problems by mimicking the behaviour of living organisms using nature-inspired mechanisms 21 . The use of EAs for feature selection has received significant attention in academia, with various algorithms being proposed, including Particle Swarm Optimization (PSO) [22][23][24] , Genetic Algorithm (GA) 25,26 , Artificial Bee Colony (ABC) 27 , Genetic Programming (GP) 28 , Gravitational Search Algorithm (GSA) 29 and Ant Colony Optimization (ACO) 30,31 . One advantage of EAs is their population-based search approach, which involves a team of entities exploring the fitness landscape to find the globally optimum solution.…”

Section: B Evolutionary Algorithmsmentioning

confidence: 99%

Bird's Eye View Feature Selection for High-Dimensional Data

Belhaouari

Shakeel

Erbad

et al. 2023

Preprint

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In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this challenge, the Bird's Eye View (BEV) feature selection technique is introduced. This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. The proposed strategy in this paper leads to improved classification performance and a reduced number of features compared to conventional methods, as demonstrated by outperforming state-of-the-art feature selection techniques across multiple benchmark datasets.

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“…Gene selection acts as a combinatorial search problem that can be steered with appropriate optimization techniques. The Memetic Cellular Genetic Algorithm (MCGA) is suggested for the cancer microarray datasets with relevant feature extraction [13].…”

Section: Previous Research Workmentioning

confidence: 99%

Data-driven based Optimal Feature Selection Algorithm using Ensemble Techniques for Classification

Ghorpade

Sonkamble

2023

IJRITCC

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The shift in paradigm with advanced Machine Learning algorithms will help to face the challenges such as computational power, training time, and algorithmic stability. The individual feature selection techniques, hardly give the appropriate feature subsets, that might be vulnerable to the variations induced at the input data and thus led to wrong conclusions. An expedient technique should be designed for approximating the feature relevance to improve the performance for the data. Unlike the prevailing techniques, the novelty of the proposed Data-driven based Optimal Feature Selection (DOFS) algorithm is the optimal k-value ‘kf’ determined by the data for effective feature selection that minimizes the computational complexity and expands the prediction power using the gradient descent method. The experimental analysis of proposed algorithm is demonstarted with ensemble techniques for the non-communicable disease such as diabetes mellitus dataset produces an accuracy of 80.80%, whereas comparative performance analysis for benchmark dataset depicts the improved accuracy of 86.03%.

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A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

Cited by 9 publications

References 34 publications

Bird’s Eye View feature selection for high-dimensional data

Bird’s Eye View feature selection for high-dimensional data

Bird's Eye View Feature Selection for High-Dimensional Data

Data-driven based Optimal Feature Selection Algorithm using Ensemble Techniques for Classification

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