[PDF] Dimensionality Reduction using GA-PSO | Semantic Scholar (2024)

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Topics

Feature Selection (opens in a new tab)Particle Swarm Optimization (opens in a new tab)Classification Accuracy (opens in a new tab)Leave-one-out Cross-validation (opens in a new tab)K-nearest Neighbors (opens in a new tab)Dimensionality Reduction (opens in a new tab)Genetic Algorithms (opens in a new tab)Machine Learning (opens in a new tab)

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A classification accuracy-based fitness function is proposed by gray-wolf optimizer to find optimal feature subset and proves much robustness against initialization in comparison with PSO and GA optimizers.

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The proposed system for feature selection is proposed using a sine cosine algorithm and shows an advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.

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The proposed system for feature selection based on firefly algorithm (FFA) optimization proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.

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An innovative approach for feature selection based on chicken swarm optimization

The proposed CSO algorithm, a new bio-inspired algorithm mimicking the hierarchal order of the chicken swarm and the behaviors of chicken swarm, can efficiently extract the chickens' swarm intelligence to optimize problems.

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The result of this study shows that using ABC and SVM is suitable for reducing the dimension of data and this approach provides efficient classification with high accuracy.

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Hybrid Monkey Algorithm with Krill Herd Algorithm optimization for feature selection
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    Computer Science

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The proposed system was tested on 18 data sets and proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.

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A new approach is presented to feature extraction in which feature selection, feature extraction, and classifier training are performed simultaneously using a genetic algorithm.

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The proposed optimization algorithm obtained promising results in terms of classification accuracy as the proposed system is able to recognize 91.59 % of the test set correctly and is more efficient in most experiments when comparing with GA and PSO.

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