An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms

Vincent, Amala Mary; Jidesh, P.

doi:10.1038/s41598-023-32027-3

Cited by 24 publications

(3 citation statements)

References 61 publications

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“…As one of the most frequent evolutionary algorithms, GA utilizes selection, crossover, and mutation operations to solve the global optimum [40]. It usually adopts a random initialization to generate the original population for the potential configuration of hyperparameters [41], requiring less prior knowledge about initial hyperparameter configuration. After continuous evolutions, the individual with the best fitness will be determined as the globally optimal solution.…”

Section: Hyperparameter Tuning Of Deep Modelsmentioning

confidence: 99%

Machining surface roughness detection by adaptive deep fusion capsule network with low illumination and noise robustness

Huang,

Zhang,

Shao

et al. 2023

Meas. Sci. Technol.

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Surface roughness measurement is of great importance for monitoring machining quality. To address the performance degradation of deep models caused by low illumination and ambient noise, this study proposes a non-contact surface roughness detection method based on an adaptive deep fusion capsule network (ADFCNet) model. Firstly, principal competent analysis-based color image enhancement is employed to augment initial surface images collected from normal illumination. Then, consisting of a deep multi-model fusion for high-level feature representation and a capsule classifier for roughness recognition, the ADFCNet model is designed to detect roughness grades by using workpiece surface images. The key hyperparameters of the model are automatically determined by a genetic algorithm. Finally, laboratory and factory experiments under low illumination are carried out to validate the effectiveness and superiority of the proposed method. Experimental results demonstrate that the proposed method has strong low-illumination and noise robustness and generalization capability, indicating a better application prospect in actual scenarios.

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Section: Hyperparameter Tuning Of Deep Modelsmentioning

confidence: 99%

Machining surface roughness detection by adaptive deep fusion capsule network with low illumination and noise robustness

Huang,

Zhang,

Shao

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The gait data were separated into training and testing sets based on the subjects for the traditional machine learning models in a similar manner as described above for the LSTM approach. The machine learning models were tuned using a Bayesian algorithm and 5fold Cross-validation [53]- [55]. For missing values imputation, we grouped the gait data by subject and assigned the mean of the gait feature to the missing value [56].…”

Section: Standard Machine Learning Models Using the Extracted Gait Ch...mentioning

confidence: 99%

Diagnosis of Disease Affecting Gait with a Body Acceleration-Based Model Using Reflected Marker Data for Training and a Wearable Accelerometer for Implementation.

Alsaleem

Takallou

Fallahtafti

et al. 2023

Preprint

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This paper demonstrates the value of a framework for processing data on body acceleration as a uniquely valuable tool for diagnosing diseases that affect gait early. As a case study, we used this model to identify individuals with peripheral artery disease (PAD) and distinguish them from those without PAD. The framework uses acceleration data extracted from anatomical reflective markers placed in different body locations to train the diagnostic models and a wearable accelerometer carried at the waist for validation. Reflective marker data have been used for decades in studies evaluating and monitoring human gait. They are widely available for many body parts but are obtained in specialized laboratories. On the other hand, wearable accelerometers enable diagnostics outside lab conditions. Models trained by raw marker data at the sacrum achieve an accuracy of 92% in distinguishing PAD patients from non-PAD controls. This accuracy drops to 28% when data from a wearable accelerometer at the waist validate the model. This model was enhanced by using features extracted from the acceleration rather than the raw acceleration, with the marker model accuracy only dropping from 86% to 60% when validated by the wearable accelerometer data.

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“…Achieving an optimal model performance necessitates hyperparameter tuning, which typically involves multiple rounds of model training and validation, further compounding computational intricacy. However, their forward propagation (i.e., predictions) is typically swift once these models are adequately trained [58,59]. Vegetation ecological process models, on the other hand, are rooted in explicit physiological and environmental processes, typically involving a set of differential equations.…”

Section: Integrating Machine Learning and Ecological Process Models F...mentioning

confidence: 99%

Synergistic Application of Multiple Machine Learning Algorithms and Hyperparameter Optimization Strategies for Net Ecosystem Productivity Prediction in Southeast Asia

Huang,

Chen,

Sun

et al. 2023

Remote Sensing

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The spatiotemporal patterns and shifts of net ecosystem productivity (NEP) play a pivotal role in ecological conservation and addressing climate change. For example, by quantifying the NEP information within ecosystems, we can achieve the protection and restoration of natural ecological balance. Monitoring the changes in NEP enables a more profound understanding and prediction of ecosystem alterations caused by global warming, thereby providing a scientific basis for formulating policies aimed at mitigating and adapting to climate change. The accurate prediction of NEP sheds light on the ecosystem’s response to climatic variations and aids in formulating targeted carbon sequestration policies. While traditional ecological process models provide a comprehensive approach to predicting NEP, they often require extensive experimental and empirical data, increasing research costs. In contrast, machine-learning models offer a cost-effective alternative for NEP prediction; however, the delicate balance in algorithm selection and hyperparameter tuning is frequently overlooked. In our quest for the optimal prediction model, we examined a combination of four mainstream machine-learning algorithms with four hyperparameter-optimization techniques. Our analysis identified that the backpropagation neural network combined with Bayesian optimization yielded the best performance, with an R2 of 0.68 and an MSE of 1.43. Additionally, deep-learning models showcased promising potential in NEP prediction. Selecting appropriate algorithms and executing precise hyperparameter-optimization strategies are crucial for enhancing the accuracy of NEP predictions. This approach not only improves model performance but also provides us with new tools for a deeper understanding of and response to ecosystem changes induced by climate change.

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An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms

Cited by 24 publications

References 61 publications

Machining surface roughness detection by adaptive deep fusion capsule network with low illumination and noise robustness

Machining surface roughness detection by adaptive deep fusion capsule network with low illumination and noise robustness

Diagnosis of Disease Affecting Gait with a Body Acceleration-Based Model Using Reflected Marker Data for Training and a Wearable Accelerometer for Implementation.

Synergistic Application of Multiple Machine Learning Algorithms and Hyperparameter Optimization Strategies for Net Ecosystem Productivity Prediction in Southeast Asia

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