Optimal Entropy Genetic Fuzzy-C-Means SMOTE (OEGFCM-SMOTE)

Moutaouakil, Karim El; Roudani, Mouhamed; Ouissari, Abdellatif El

doi:10.1016/j.knosys.2022.110235

Cited by 23 publications

(3 citation statements)

References 56 publications

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“…The proposed algorithm, within the context of the boosting algorithm, integrates two data sampling algorithms and two base classifiers. El Moutaouakil et al 36 . proposed Optimal Entropy Genetic Fuzzy-C-Means SMOTE (OEGFCM-SMOTE), for handling imbalanced datasets in classification problems.…”

Section: Literature Reviewmentioning

confidence: 99%

A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data

Salehi,

Khedmati

2024

Sci Rep

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In this paper, a Cluster-based Synthetic minority oversampling technique (SMOTE) Both-sampling (CSBBoost) ensemble algorithm is proposed for classifying imbalanced data. In this algorithm, a combination of over-sampling, under-sampling, and different ensemble algorithms, including Extreme Gradient Boosting (XGBoost), random forest, and bagging, is employed in order to achieve a balanced dataset and address the issues including redundancy of data after over-sampling, information loss in under-sampling, and random sample selection for sampling and sample generation. The performance of the proposed algorithm is evaluated and compared to different state-of-the-art competing algorithms based on 20 benchmark imbalanced datasets in terms of the harmonic mean of precision and recall (F1) and area under the receiver operating characteristics curve (AUC) measures. Based on the results, the proposed CSBBoost algorithm performs significantly better than the competing algorithms. In addition, a real-world dataset is used to demonstrate the applicability of the proposed algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data

Salehi,

Khedmati

2024

Sci Rep

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“…A primary area of research in learning from empirical data through support vector machines (SVMs) and addressing classification and regression issues is the development of incremental learning schemes when the size of the training dataset is massive [5]. Out of many possible candidates, avoiding the usage of regular quadratic programming (QP) solvers, the two learning methods gaining attention recently are iterative single-data algorithms (ISDA) and sequential minimal optimization (SMO) [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

OPT-RNN-DBSVM: OPTimal Recurrent Neural Network and Density-Based Support Vector Machine

El Moutaouakil,

El Ouissari,

Olaru

et al. 2023

Mathematics

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When implementing SVMs, two major problems are encountered: (a) the number of local minima of dual-SVM increases exponentially with the number of samples and (b) the computer storage memory required for a regular quadratic programming solver increases exponentially as the problem size expands. The Kernel-Adatron family of algorithms, gaining attention recently, has allowed us to handle very large classification and regression problems. However, these methods treat different types of samples (i.e., noise, border, and core) in the same manner, which makes these algorithms search in unpromising areas and increases the number of iterations as well. This paper introduces a hybrid method to overcome such shortcomings, called the Optimal Recurrent Neural Network and Density-Based Support Vector Machine (Opt-RNN-DBSVM). This method consists of four steps: (a) the characterization of different samples, (b) the elimination of samples with a low probability of being a support vector, (c) the construction of an appropriate recurrent neural network to solve the dual-DBSVM based on an original energy function, and (d) finding the solution to the system of differential equations that govern the dynamics of the RNN, using the Euler–Cauchy method involving an optimal time step. Density-based preprocessing reduces the number of local minima in the dual-SVM. The RNN’s recurring architecture avoids the need to explore recently visited areas. With the optimal time step, the search moves from the current vectors to the best neighboring support vectors. It is demonstrated that RNN-SVM converges to feasible support vectors and Opt-RNN-DBSVM has very low time complexity compared to the RNN-SVM with a constant time step and the Kernel-Adatron algorithm–SVM. Several classification performance measures are used to compare Opt-RNN-DBSVM with different classification methods and the results obtained show the good performance of the proposed method.

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“…Several studies were conducted on the type 2 diabetes in order to reduce the burden of this disease [4], [10], [11], [12], [13]. Among these studies, we can mention the dynamic studies that focus on the study of the phenomenon and the study of the stability of the proposed systems, and other studies that use optimal control strategies to reduce the negative effects of this disease [14], [9], [15], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm applied to fractional optimal control of a diabetic patient

El Ouissari,

El Moutaouakil

2023

Ufimsk. Mat. Zh.

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Diabetes is a dangerous disease that increases in incidence every year. The aim of this paper is to present and analyze the model of diabetes and its complications with the fractional derivative of Caputo, namely, we propose a mathematical model with a fractional derivative of the type 2 diabetes. The positivity and boundedness of the solutions is demonstrated by the Laplace transform method. We study the existence and uniqueness of the solution of the system. We use the genetic algorithm (GA) to solve the fractional differential equation model and to characterize the optimal control and this is an efficient and simple metaheuristic method to implement. Simulations of the total number of diabetics with the different values of a parameter 𝛼 show that the combined control strategy leads to a significant decrease. The simulation results also show that the number of uncomplicated diabetics in the fractional model, for the different fractional values of 𝛼, decreases more rapidly than the integer derivative model.

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Optimal Entropy Genetic Fuzzy-C-Means SMOTE (OEGFCM-SMOTE)

Cited by 23 publications

References 56 publications

A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data

A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data

OPT-RNN-DBSVM: OPTimal Recurrent Neural Network and Density-Based Support Vector Machine

Genetic algorithm applied to fractional optimal control of a diabetic patient

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