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Multiclass feature selection with metaheuristic optimization algorithms: a review
Published 2022Subjects: “…Feature selection…”
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An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection
Published 2022“…The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. …”
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Development of Lévy flight-based reptile search algorithm with local search ability for power systems engineering design problems
Published 2022“…Therefore, the paper discusses the development of the Lévy flight-based reptile search algorithm with local search capability and evaluates its potential against challenging power systems engineering optimization problems. …”
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Image Local Features Description Through Polynomial Approximation
Published 2019“…The rotation invariance is achieved by aligning the local patch around the Harris corner through the dominant orientation shift algorithm. …”
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Arithmetic optimization algorithm: a review and analysis
Published 2024“…As a result, it can perform diverse optimization tasks in different search spaces. This article reviews the behaviors of mathematics operations that inspire the main features of AOA, which is a metaheuristic algorithm. …”
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Optimizing Document Classification: Unleashing the Power of Genetic Algorithms
Published 2023“…Additionally, our proposed model optimizes the features using a genetic algorithm. Optimal feature selection performances a crucial role in this domain, enhancing the overall accuracy of the document classification system while reducing the time complexity associated with selecting the most relevant features from this large-dimensional space. …”
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EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach
Published 2019“…A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. …”
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Particle swarm optimization algorithm: review and applications
Published 2024“…Particle swarm optimization (PSO) is a heuristic global optimization technique and an optimization algorithm that is swarm intelligence-based. …”
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An Improved Genghis Khan Optimizer based on Enhanced Solution Quality Strategy for Global Optimization and Feature Selection Problems
Published 2024“…To assess the efficacy of the I-GKSO, it has been subjected to comparisons with multiple different algorithms. The trials conducted using FS datasets yield a quantitative consideration of the I-GKSO's capacity to attain the most optimal subset of features. …”
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UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data
Published 2024“…To improve the effectiveness and efficiency of the UniBFS algorithm, Redundant Features Elimination algorithm (RFE) is presented in this paper. …”
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Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Published 1992“…We present three parallel physical optimization algorithms for mapping data to distributed-memory multiprocessors, concentrating on irregular loosely synchronous problems. …”
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StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features
Published 2024“…Additionally, we applied the local interpretable model-agnostic explanations (LIME) algorithm to understand the contribution of selected features to the overall prediction. …”
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Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering
Published 2024“…A self-organizing map is one of the well-known unsupervised neural network algorithms used for preserving typologies during mapping from the input space (high-dimensional) to the display (low-dimensional).An algorithm called Local Adaptive Receptive Field Dimension Selective Self-Organizing Map 2 is a modified form of a self-organizing Map to cater different data types in the dataset. …”
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