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Multi-surrogates and multi-points infill strategy-based global optimization method
Surrogate-based global optimization (SBGO) methods are widely used to deal with the computationally expensive black-box optimization problems. In...
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A novel ensemble model using artificial neural network for predicting wave-induced forces on coastal bridge decks
Due to the effects of climate change, coastal engineering structures are more vulnerable to the wave forces caused by natural hazards, especially for...
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Omni: automated ensemble with unexpected models against adversarial evasion attack
ContextMachine learning-based security detection models have become prevalent in modern malware and intrusion detection systems. However, previous...
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Towards Efficient Multiobjective Hyperparameter Optimization: A Multiobjective Multi-fidelity Bayesian Optimization and Hyperband Algorithm
Develo** an efficient solver for hyperparameter optimization (HPO) can help to support the environmental sustainability of modern AI. One popular...
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Hyperparameter Tuning of Random Forests Using Radial Basis Function Models
This paper considers the problem of tuning the hyperparameters of a random forest (RF) algorithm, which can be formulated as a discrete black-box...
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LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity
We propose transferability from Large Geometric Vicinity (LGV), a new technique to increase the transferability of black-box adversarial attacks. LGV...
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Optimally Weighted Ensembles for Efficient Multi-objective Optimization
The process of industrial design engineering is often involved with the simultaneous optimization of multiple expensive objectives. The surrogate...
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Low-Cost Behavioral Modeling of Antennas by Dimensionality Reduction and Domain Confinement
Behavioral modeling has been rising in importance in modern antenna design. It is primarily employed to diminish the computational cost of procedures...
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Decision-based evasion attacks on tree ensemble classifiers
Learning-based classifiers are found to be susceptible to adversarial examples. Recent studies suggested that ensemble classifiers tend to be more...
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A surrogate-assisted bi-swarm evolutionary algorithm for expensive optimization
This article introduces an efficient surrogate-assisted bi-swarm evolutionary algorithm (SABEA) with hybrid and ensemble strategies for...
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Radial Basis Function and Bayesian Methods for the Hyperparameter Optimization of Classification Random Forests
The hyperparameter optimization of a random forest (RF) is a discrete black-box optimization problem that aims to find the settings of the...
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The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning
Due to the proliferation of malware, defenders are increasingly turning to automation and machine learning as part of the malware detection...
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A multi-fidelity active learning method for global design optimization problems with noisy evaluations
A multi-fidelity (MF) active learning method is presented for design optimization problems characterized by noisy evaluations of the performance...
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Co-imagination of Behaviour and Morphology of Agents
The field of robot learning has made great advances in develo** behaviour learning methodologies capable of learning policies for tasks ranging...
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Leveraging Muscular Fitness Surrogates to Classify Cardiorespiratory Fitness Status in Youth: A Supervised Machine Learning Approach
Cardiorespiratory fitness (CRF) is linked with anxiety, depression, and cardiovascular disease risk. Assessing CRF is time consuming,...
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Local Multi-label Explanations for Random Forest
Multi-label classification is a challenging task, particularly in domains where the number of labels to be predicted is large. Deep neural networks...
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An Architecture and a New Deep Learning Method for Head and Neck Cancer Prognosis by Analyzing Serial Positron Emission Tomography Images
In the U.S. it is estimated that there are more than 20,000 cases of head and neck cancers per year. Radiomics is a much discussed topic in nuclear...
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Real-time prediction of gas flow dynamics in diesel engines using a deep neural operator framework
The objective of this work is to address the need for fast and accurate models for analyzing transient gas flow dynamics in diesel engines. We employ...
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Expedited Machine-Learning-Based Global Design Optimization of Antenna Systems Using Response Features and Multi-fidelity EM Analysis
The design of antenna systems poses a significant challenge due to stringent performance requirements dictated by contemporary applications and the...
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Biological Strategies ParetoGP Enables Analysis of Wide and Ill-Conditioned Data from Nonlinear Systems
Genetic, proteomic, and other biologically derived data sets are often ill-conditioned with many more variables than data records. Furthermore, the...