Classification of attention levels using a Random Forest algorithm optimized with Particle Swarm Optimization

Bedolla-Ibarra, María Guadalupe; Cabrera-Hernandez, Maria del Carmen; Aceves-Fernández, Marco Antonio; Tovar-Arriaga, Saúl

doi:10.1007/s12530-022-09444-2

Cited by 10 publications

(5 citation statements)

References 34 publications

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“…(2) The classification result of random forest depends on the voting of multiple meta-classifiers, and the classification accuracy of the algorithm is mainly determined by two aspects, one is the correlation of any two meta-classifiers, the greater the correlation, the lower the classification accuracy; the second is the lack of processing ability for continuity fields, and the classification accuracy is also limited [15].…”

Section: Problems Of the Random Forest Algorithmmentioning

confidence: 99%

Intrusion Detection Classification Based on Random Forest

2022

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In recent years, network security has become increasingly severe and network intrusions are frequent. On the one hand, it is difficult to capture intrusions because of the increasingly large volume of computer networks and complex network topology, and on the other hand, it is more difficult to capture intrusions because the characteristics of intrusions are becoming more diverse and complex. The purpose of this paper is to study intrusion detection classification based on random forest. After constructing a high-precision decision tree, the double failure metric is used as the distance between two decision trees, and the k-means++ algorithm is used to select high-precision decision trees with a certain degree of independence to form the final random forest. The improved random forest algorithm for setting up clustering and the random forest algorithm were experimented as algorithm comparisons. The KDD-NSL dataset was selected and the experimental results were compared to demonstrate the effectiveness of the random forest algorithm based on gradient boosting. The experimental results show that the improved random forest model has a significant reduction in decision tree size and a significant increase in diversity due to the k-means++ algorithm for clustering and extracting cluster centers.

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Section: Problems Of the Random Forest Algorithmmentioning

confidence: 99%

Intrusion Detection Classification Based on Random Forest

2022

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“…At the same time, a large number of pollutants discharged into rivers and lakes will cause harm to human health and are difficult to degrade. Other pollutants, such as suspended solids and organic matter, will enter the groundwater through surface water to pollute the water quality and worsen the ecological balance of the water body and human health problems [11][12]. At present, the construction of urban sewage treatment plants in China is large in scale and widely distributed.…”

Section: Current Situation Of Water Pollutionmentioning

confidence: 99%

Technical Design of Water Pollution Prevention Project Based on Particle Swarm Optimization

2022

WPPCP

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In the water pollution prevention project, particle swarm optimization is a very practical method, which gathers multiple individuals into a group. It can effectively solve traditional problems. This paper mainly introduces the water pollution control technology and control theory and basic steps based on the characteristics of nuclear particle number and diversity, multi species clustering, etc. Then, it summarizes, analyzes and studies the existing literature on its application and shortcomings, and gives suggestions for improvement. It further explains from the design idea to the algorithm flow, and applies this method to practical projects to reduce costs while ensuring accuracy. Next, based on the research of particle swarm optimization algorithm, this paper designs the technical framework of water pollution prevention and control engineering, and conducts simulation tests on the performance of this algorithm. The test results show that in water pollution prevention and control engineering, through the comparative analysis of the performance of particle swarm optimization algorithm, it is found that the particle swarm optimization algorithm has a fast running time and high iteration efficiency, because it has good global optimization ability, And its performance is also very stable and reliable. Therefore, these factors should be fully considered and utilized in the design process to improve the technical level of water pollution treatment so as to achieve the goal of environmental protection in a real sense.

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“…The results showed that the improved model had significant advantages relating to its classification accuracy and convergence speed. Bedolla-Ibarra et al [46] optimized the random forest algorithm for attention-level classification using the particle swarm optimization algorithm and achieved a high accuracy rate. Dogan et al [47] optimized an extreme learning machine using the Salp Swarm Algorithm and classified images of 14 different types of dry bean cultivars with an accuracy rate of 91.43%.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Classification of Maize Seeds Based on RGB and Hyperspectral Data with Improved Grey Wolf Optimization Algorithms

Bi,

Zhang,

Chen

et al. 2024

Agronomy

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Ensuring the security of germplasm resources is of great significance for the sustainable development of agriculture and ecological balance. By combining the morphological characteristics of maize seeds with hyperspectral data, maize variety classification has been achieved using machine learning algorithms. Initially, the morphological data of seeds are obtained from images, followed by the selection of feature subsets using Recursive Feature Elimination (RFE) and Select From Model (SFM) methods, indicating that features selected by RFE exhibit better performance in maize seed classification. For hyperspectral data (350–2500 nm), Competitive Adaptive Re-weighted Sampling (CARS) and the Successive Projections Algorithm (SPA) are employed to extract feature wavelengths, with the SPA algorithm demonstrating superior performance in maize seed classification tasks. Subsequently, the two sets of data are merged, and a Random Forest (RF) classifier optimized by Grey Wolf Optimization (GWO) is utilized. Given the limitations of GWO, strategies such as logistic chaotic mapping for population initialization, random perturbation, and final replacement mechanisms are incorporated to enhance the algorithm’s search capabilities. The experimental results show that the proposed ZGWO-RF model achieves an accuracy of 95.9%, precision of 96.2%, and recall of 96.1% on the test set, outperforming the unimproved model. The constructed model exhibits improved identification effects on multi-source data, providing a new tool for non-destructive testing and the accurate classification of seeds in the future.

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Classification of attention levels using a Random Forest algorithm optimized with Particle Swarm Optimization

Cited by 10 publications

References 34 publications

Intrusion Detection Classification Based on Random Forest

Intrusion Detection Classification Based on Random Forest

Technical Design of Water Pollution Prevention Project Based on Particle Swarm Optimization

Non-Destructive Classification of Maize Seeds Based on RGB and Hyperspectral Data with Improved Grey Wolf Optimization Algorithms

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