Learning automata based particle swarm optimization for solving class imbalance problem

Chakraborty, Anuran; Ghosh, Kushal Kanti; De, Rajonya; Cuevas, Erik; Sarkar, Ram

doi:10.1016/j.asoc.2021.107959

Cited by 16 publications

(5 citation statements)

References 38 publications

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“…It can be seen that if the intensity of the market trend increases, the AVOA-SSA algorithm can predict a strong upward trend. When compared to many alternative optimization algorithms, the AVOA-SSA algorithm has, once again, attained the lowest error rate, as can be shown in Table (18). The error rate obtained from the AVOA-SSA algorithm was significantly lower than different optimization algorithms.…”

Section: Results and Discussion For Stock Market Predictionmentioning

confidence: 81%

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An Improved hybrid Salp Swarm Optimization and African Vulture Optimization Algorithm for Global Optimization Problems and Its Applications in Stock Market Prediction

Alizadeh

Gharehchopogh

Màsdàrí

et al. 2023

Preprint

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Optimization is necessary for solving and improving the solution of various complex problems. Every meta-heuristic algorithm can have a weak point, and multiple mechanisms and methods can be used to overcome these weaknesses. We can use hybrid meta-heuristic algorithms to arrive at an efficient algorithm. This paper presents a new and intelligent approach by hybridizing meta-heuristic algorithms and using different mechanisms simultaneously without significantly increasing the time complexity. For this purpose, two algorithms, Salp Swarm Optimization(SSO) and the African Vulture Optimization Algorithm (AVOA) have been hybridized. And to improve the optimization process of the Modified Choice Function and Learning Automata mechanisms. In addition, two other improving mechanisms, named Opposition-Based Learning (OBL) and β-hill climbing (BHC) technique, have been presented and integrated with the AVOA-SSA algorithm. Fifty-two standard benchmarks were used to test and evaluate the AVOA-SSA algorithm. Finally, an improved version of the Extreme Learning Machine(ELM) classifier has been used with real stock market data for stock market prediction. The obtained results indicate the excellent and acceptable performance of the AVOA-SSA algorithm in `solving optimization problems and has been able to achieve high-quality solutions.

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Section: Results and Discussion For Stock Market Predictionmentioning

confidence: 81%

“…In [18], the author presented an improved version of Particle Swarm Optimization (PSO) using learning automata. In this approach, the learning automata mechanism used throughout the optimization process interacts with the problem environment to intelligently optimize the parameters of the PSO algorithm.…”

mentioning

confidence: 99%

An Improved hybrid Salp Swarm Optimization and African Vulture Optimization Algorithm for Global Optimization Problems and Its Applications in Stock Market Prediction

Alizadeh

Gharehchopogh

Màsdàrí

et al. 2023

Preprint

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“…Given a D-dimensional space, the inertial weight w controls the velocities c 1 and c 2 to balance exploration (searching for new potential regions) with exploitation (tuning the current searching area). The bird flock structure of PSO algorithms updates the next potential position of each particle using not only the experience itself but diverse experiences from other particles in the swamp, which increases the speed of convergence and the handling of noisy datasets [67,68].…”

Section: Discussionmentioning

confidence: 99%

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Tran,

Nguyen,

Pham

et al. 2024

Geosciences

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Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R2 = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments.

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“…Developing a proficient learning model utilizing machine learning approaches in datasets characterized by an imbalanced class distribution represents a difficult challenge. The problem of imbalanced class distribution can frequently be observed in practical classification tasks like spam detection (Li & Liu, 2018), disease detection (Chakraborty et al, 2021; Fernandes et al, 2019; Fujiwara et al, 2020; Li et al, 2017; Ramaswamy & Mukherjee, 2020; Sonak et al, 2016; Tallo & Musdholifah, 2018), credit card fraud detection (Dal Pozzolo et al, 2014; Wei et al, 2013), and cyber security (Bagui & Li, 2021). Generally, a serious class imbalance problem is encountered as a result of the low incidence rates of some diseases in health datasets.…”

Section: Related Workmentioning

confidence: 99%

A heuristic‐based hybrid sampling method using a combination of SMOTE and ENN for imbalanced health data

Nizam‐Ozogur,

Orman

2024

Expert Systems

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Health datasets often exhibit class imbalance, with healthy individuals being the majority class and patients being the minority class. As in many applications, machine learning methods are frequently used in the prediction and detection of diseases. The class imbalance presents a significant challenge for machine learning classifiers, particularly in their ability to accurately and effectively classify data from minority and majority classes. Therefore, data preprocessing is crucial before classifying the imbalanced data. In this study, we present GASMOTEPSO_ENN method that combines the synthetic minority oversampling technique (SMOTE) and edited nearest neighbour (ENN) algorithms using genetic algorithm (GA) and particle swarm optimization (PSO) heuristics as a preprocessing method to classify the imbalanced health datasets. In the experiments, chronic kidney disease (CKD), cerebral stroke prediction (CSP), and PIMA Indian diabetes (PID) datasets were utilized to assess the performance of the proposed method with metrics derived from the confusion matrix. The GASMOTEPSO_ENN method can classify the various diseases into different two classes of patients and healthy individuals with acceptable Matthews correlation coefficient (MCC) metric using the machine learning algorithms (Logistic regression (LR) 1.00 for CKD dataset, extreme gradient boosting (XGBoost) 0.94 for CSP dataset, and support vector machine (SVM) 0.87 for PID dataset). Moreover, the proposed method also performed well with other metrics in all datasets, and the analysis of the model results in relation to existing literature reveals that the proposed model demonstrably produces superior results.

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Learning automata based particle swarm optimization for solving class imbalance problem

Cited by 16 publications

References 38 publications

An Improved hybrid Salp Swarm Optimization and African Vulture Optimization Algorithm for Global Optimization Problems and Its Applications in Stock Market Prediction

An Improved hybrid Salp Swarm Optimization and African Vulture Optimization Algorithm for Global Optimization Problems and Its Applications in Stock Market Prediction

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

A heuristic‐based hybrid sampling method using a combination of SMOTE and ENN for imbalanced health data

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