Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification

Haritha, K.; Judy, M. V.; Παπαγεωργιου, Κωνσταντινοσ; Gerogiannis, Vassilis C.; Papageorgiou, Elpiniki I.

doi:10.3390/a15100383

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…Sajal et al [ 57 ] provided a method based on KNN, SVM, and NB, with accuracies of 90.50%, 87.00%, and 81.00% for five levels of classification in tremor analysis. Furthermore, Haritha et al [ 58 ] obtained 76.20%, 86.71%, 91.83%, 82.90%, and 87.03% accuracy utilizing NB, DT, RF, MLP, and LR, respectively. Abayomi-Alli et al [ 59 ] demonstrated a Bidirectional Long Short-Term Memory (BiLSTM) for the UCI PD dataset, and their model achieved an accuracy of 82.86% with the original data.…”

Section: Resultsmentioning

confidence: 99%

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Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

Elshewey

Shams

El-Rashidy

et al. 2023

Sensors

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Parkinson’s disease (PD) has become widespread these days all over the world. PD affects the nervous system of the human and also affects a lot of human body parts that are connected via nerves. In order to make a classification for people who suffer from PD and who do not suffer from the disease, an advanced model called Bayesian Optimization-Support Vector Machine (BO-SVM) is presented in this paper for making the classification process. Bayesian Optimization (BO) is a hyperparameter tuning technique for optimizing the hyperparameters of machine learning models in order to obtain better accuracy. In this paper, BO is used to optimize the hyperparameters for six machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Ridge Classifier (RC), and Decision Tree (DT). The dataset used in this study consists of 23 features and 195 instances. The class label of the target feature is 1 and 0, where 1 refers to the person suffering from PD and 0 refers to the person who does not suffer from PD. Four evaluation metrics, namely, accuracy, F1-score, recall, and precision were computed to evaluate the performance of the classification models used in this paper. The performance of the six machine learning models was tested on the dataset before and after the process of hyperparameter tuning. The experimental results demonstrated that the SVM model achieved the best results when compared with other machine learning models before and after the process of hyperparameter tuning, with an accuracy of 92.3% obtained using BO.

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Section: Resultsmentioning

confidence: 99%

“… Comparison between the proposed model and the recent approaches [ 56 , 57 , 58 , 59 , 60 ] using the same standard PD dataset. …”

Section: Figurementioning

confidence: 99%

Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

Elshewey

Shams

El-Rashidy

et al. 2023

Sensors

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“…The final state vector, representing the final global solution, was derived by amalgamating the aforementioned partial outcomes. The parallel learning of the FCM is depicted in Figure 3, which was adopted from [60].…”

Section: Parallelization Of Fcmmentioning

confidence: 99%

“…partial outcomes. The parallel learning of the FCM is depicted in Figure 3, which was adopted from [60].…”

Section: Parallel Genetic Algorithm To Determine the Community Struct...mentioning

confidence: 99%

Distributed Genetic Algorithm for Community Detection in Large Graphs with a Parallel Fuzzy Cognitive Map for Focal Node Identification

et al. 2023

Self Cite

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This study addresses the importance of focal nodes in understanding the structural composition of networks. To identify these crucial nodes, a novel technique based on parallel Fuzzy Cognitive Maps (FCMs) is proposed. By utilising the focal nodes produced by the parallel FCMs, the algorithm efficiently creates initial clusters within the population. The community discovery process is accelerated through a distributed genetic algorithm that leverages the focal nodes obtained from the parallel FCM. This approach mitigates the randomness of the algorithm, addressing the limitations of the random population selection commonly found in genetic algorithms. The proposed algorithm improves the performance of the genetic algorithm by enabling informed decision making and forming a better initial population. This enhancement leads to improved convergence and overall algorithm performance. Furthermore, as graph sizes grow, traditional algorithms struggle to handle the increased complexity. To address this challenge, distributed algorithms are necessary for effectively managing larger data sizes and complexity. The proposed method is evaluated on diverse benchmark networks, encompassing both weighted and unweighted networks. The results demonstrate the superior scalability and performance of the proposed approach compared to the existing state-of-the-art methods.

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Sparse and regression learning of large-scale fuzzy cognitive maps based on adaptive loss function

Zhou,

Ma,

Xing

et al. 2024

Appl Intell

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Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification

Cited by 5 publications

References 29 publications

Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

Distributed Genetic Algorithm for Community Detection in Large Graphs with a Parallel Fuzzy Cognitive Map for Focal Node Identification

Sparse and regression learning of large-scale fuzzy cognitive maps based on adaptive loss function

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