Fuzzy clustering decomposition of genetic algorithm-based instance selection for regression problems

Kordos, Mirosław; Blachnik, Marcin; Scherer, Rafał

doi:10.1016/j.ins.2021.12.016

Cited by 27 publications

(3 citation statements)

References 39 publications

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“…The density-based algorithm is represented by DB-SCAN (Shinde et al, 2022 ) CGCA (Kowalski and Jeczmionek, 2022 ) and other similar methods. Hierarchical clustering methods can use two different strategies : top-down and bottom-up (also known as agglomerative clustering) (Kordos et al, 2022 ). Finally, the most popular is the partition method using k-means algorithm (Ma et al, 2021d ; Sathyamoorthy et al, 2022 ), although these methods can achieve better clustering effect, because the emergency rescue pay attention to efficient and fast, so we use the PSO on the line clustering, because the PSO algorithm has a local optimal solution and global optimal solution, in order to avoid local convergence, we adjust the particle motion through the global optimal, in addition to PSO aggregation speed is also very fast.…”

Section: Related Workmentioning

confidence: 99%

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication

Cao

Liu

et al. 2023

Front. Neurorobot.

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Communication infrastructure is damaged by disasters and it is difficult to support communication services in affected areas. UAVs play an important role in the emergency communication system. Due to the limited airborne energy of a UAV, it is a critical technical issue to effectively design flight routes to complete rescue missions. We fully consider the distribution of the rescue area, the type of mission, and the flight characteristics of the UAV. Firstly, according to the distribution of the crowd, the PSO algorithm is used to cluster the target-POI of the task area, and the neural collaborative filtering algorithm is used to prioritize the target-POI. Then we also design a Trans-UTPA algorithm. Based on MAPPO 's policy network and value function, we introduce transformer model to make Trans-UTPA's policy learning have no action space limitation and can be multi-task parallel, which improves the efficiency and generalization of sample processing. In a three-dimensional space, the UAV selects the emergency task to be performed (data acquisition and networking communication) based on strategic learning of state information (location information, energy consumption information, etc.) and action information (horizontal flight, ascent, and descent), and then designs the UAV flight path based on the maximization of the global value function. The experimental results show that the performance of the Trans-UTPA algorithm is further improved compared with the USCTP algorithm in terms of the success rate of each UAV reaching the target position, the number of collisions, and the average reward of the algorithm. Among them, the average reward of the algorithm exceeds the USCTP algorithm by 13%, and the number of collisions is reduced by 60%. Compared with the heuristic algorithm, it can cover more target-POIs, and has less energy consumption than the heuristic algorithm.

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Section: Related Workmentioning

confidence: 99%

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication

Cao

Liu

et al. 2023

Front. Neurorobot.

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“…The major drawbacks of GA-based instance selection where it is computationally complex and when the size of the dataset is growing, the performance is badly affected. This was addressed through Fuzzy clustering by dividing the dataset into regions and instance selection based on GA was done in each cluster and the final result was obtained through ensemble voting 18 . Under Sampling approach called Clustering Based Instance Selection (CBIS) was designed to overcome the problems of class-imbalanced datasets which tends to affect the classifier in classifying the minority class from the majority class.…”

Section: Metaheuristic Algorithms For Instance Selectionmentioning

confidence: 99%

Handling the Class Imbalance Problem using an improved Sine Cosine Algorithm for Optimal Instance Selection

Moorthy

Arikumar²,

Prathiba

et al. 2023

Preprint

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Class imbalance is a significant problem that is biased, exhibiting excellent performance towards the majority classes in the dataset while inhibiting inferior performance toward minority classes. When dealing with real-world issues, in particular healthcare problems, this kind of biased nature affects classification accuracy. Thus, a class imbalance is a danger that directly affects the effectiveness of any classification algorithm. The Improved Binary Sine Cosine Algorithm (IBSCA) has been used in this work to identify the subset of the majority class in the best possible way. The proposed IBSCA makes some enhancements over the conventional Binary Sine Cosine Algorithm (BSCA) to address the issue of premature convergence with the local optimal solutions. Intending to improve the classification accuracy for unbalanced datasets, the proposed IBSCA seeks to identify the optimal collection of instances from the majority class. The advised IBSCA uses a random agent’s location, which tends to devote considerable time to exploration to find the best possible set of instances. By using the geometric Mean (G-Mean) and F-Score to describe the fitness function, the proposed IBSCA aims to solve the multi-objective optimization issue. The most crucial metrics for assessing how well a classifier performs on skewed datasets are G-Meanand F-Score. Additionally, a V-shaped transfer function is used to handle the discrete nature of the class imbalance issue. On 18 datasets with different imbalance ratios taken from the KEEL repository, experimentation is conducted. Comparisons are made between the suggested IBSCA and the traditional BSCA, Binary Particle Swarm Optimization (BPSO), and Binary GreyWolf Optimization (BGWO). Additionally, the performance of the suggested IBSCA is evaluated against the top outcomes from different research papers. Metrics like Sensitivity, F-Score, G-Mean, and Area under Curve (AUC) show that the suggestedIBSCA outperforms the current algorithms. The statistical findings also demonstrate that the suggested IBSCA is more efficient than the other conventional algorithms.

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“…Optimization-based techniques: These methods select a subset of instances that maximizes a specific objective function, such as minimizing redundancy or maximizing diversity. Examples include clustering-based techniques [4], entropy-based techniques [5], and using genetic algorithms [6,7].…”

Section: Introductionmentioning

confidence: 99%

Preserving Data Distribution in Sampling and Instance Selection with Renyi's Divergence

Sadoghi-Yazdi,

Ashkezari-Toussi,

Ramezanzadeh-Yazdi

2023

Preprint

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This paper presents a novel method for sampling based on a distribution function. By leveraging Renyi's divergence criterion, a recursive formulation is directly derived from the disparity between the original and estimated distributions. The Gradient descent algorithm is employed to achieve this recursive equation, and then new samples are derived. Since this method uses the distribution of the original data, it has the ability to preserve the data distribution in the sampled dataset. In instances where the original distribution is unknown, Kernel Density Estimation is employed for estimation. Experimental results demonstrate that the proposed method effectively preserves the data distribution and prevents any alteration in the data concept while maintaining the predictability of the model over selected instances. More precisely, this method has successfully reduced the dataset size by approximately $70\%$, while the accuracy of the learning algorithm has remained unaffected.

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Fuzzy clustering decomposition of genetic algorithm-based instance selection for regression problems

Cited by 27 publications

References 39 publications

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication

Handling the Class Imbalance Problem using an improved Sine Cosine Algorithm for Optimal Instance Selection

Preserving Data Distribution in Sampling and Instance Selection with Renyi's Divergence

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