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A dynamic multi-objective optimization method based on classification strategies
The dynamic multi-objective optimization problem is a common problem in real life, which is characterized by conflicting objectives, the Pareto...
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Sample classification by selecting informative genes: a greedy multi-objective simulated annealing approach
Identifying a small subset of informative genes from a gene expression dataset is vital in sample classification. In this process, there are two...
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Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning
Resource recycling is considered necessary for sustainable development, especially in smart cities where increased urbanization and the variety of...
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Active broad learning with multi-objective evolution for data stream classification
In a streaming environment, the characteristics and labels of instances may change over time, forming concept drifts. Previous studies on data stream...
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A fine-tuning deep learning with multi-objective-based feature selection approach for the classification of text
Document classification is becoming increasingly essential for the vast number of documents available in digital libraries, emails, the Internet,...
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Garbage Classification Game Based on Bilevel Multi-objective Optimization with Fuzzy Weight
Nowadays, the bilevel multi-objective optimization problem has been received extensive attention from scholars. As two mainstream algorithms to solve...
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A novel implicit decision variable classification approach for high-dimensional robust multi-objective optimization in order scheduling
This paper efficiently addresses the high-dimensional robust order scheduling problem. A novel algorithm named dynamic cooperative coevolution based...
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Statistically aided Binary Multi-Objective Grey Wolf Optimizer: a new feature selection approach for classification
Feature selection identifies the most relevant features that help improve the classifier’s performance. The Binary Multi-Objective Grey Wolf...
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Hybrid hesitant fuzzy linguistic bi-objective binary coyote clustering based segmentation and classification for land use land cover in hyperspectral image
Land use and land cover segmentation and classification from hyperspectral image is significant in many land use inventories. Though several...
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SVM ensemble training for imbalanced data classification using multi-objective optimization techniques
One of the main problems with classifier training for imbalanced data is defining the correct learning criterion. On the one hand, we want the...
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Progressive Self-supervised Multi-objective NAS for Image Classification
We introduce a novel progressive self-supervised framework for neural architecture search. Our aim is to search for competitive, yet significantly...
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Remote sensing scene classification using visual geometry group 19 model and multi objective grasshopper optimization algorithm
Recently, the Remote Sensing Scene Classification (RSSC) has played a vital role in several applications: environment monitoring, urban planning, and...
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Sunflower seeds classification based on sparse convolutional neural networks in multi-objective scene
Generally, sunflower seeds are classified by machine vision-based methods in production, which include using photoelectric sensors to identify...
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A Scatter Search Algorithm for Multi-Criteria Inventory Classification considering Multi-Objective Optimization
Inventory management requires thousands or millions of individual transactions each year. Classification of the items influences the results of...
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A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection
Adaptive operator selection (AOS) is used to dynamically select the appropriate genic operator for offspring reproduction, which aims to improve the...
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AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification
Automated Machine Learning (AutoML) has achieved high popularity in recent years. However, most of these studies have investigated alternatives to...
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A Multi-objective Optimization Approach for the Synthesis of Granular Computing-Based Classification Systems in the Graph Domain
The synthesis of a pattern recognition system usually aims at the optimization of a given performance index. However, in many real-world scenarios,...
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Bi- and multi-objective location routing problems: classification and literature review
The facility location problem and the vehicle routing problem are highly interdependent and critical parts of any efficient and cost-effective supply...
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An effective offspring generation strategy for many-objective optimization driven by knee points under variable classification
In many-objective optimization problems, the difficulty of optimization increases as the number of targets increases. When the number of objectives...
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A resource allocation-based multi-objective evolutionary algorithm for large-scale multi-objective optimization
In large-scale multi-objective optimization problems (LSMOPs), multiple conflicting objectives and hundreds even thousands of decision variables are...
