An Approach to Hyperparameter Optimization for the Objective Function in Machine Learning

Kim, Yong‐Hoon; Chung, Mokdong

doi:10.3390/electronics8111267

Cited by 17 publications

(14 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this purpose, a BO-based hyper-parameter optimization will be applied. It uses prior information to obtain the parameter distribution, and it is widely used for machine learning models [33][34][35].…”

Section: Psomentioning

confidence: 99%

“…In order to balance the exploration and the exploitation in the selection of the next point to examine, acquisition function is used (Probability of improvement (PI) [42], Expected improvement (EI) [43], GP upper confidence bound (GP-UCB) [44]) . For a detailed analysis of BO-based hyper-parameter optimization, readers are referred to [33][34][35]. According to the results provided in [34], EI and GP-UCB functions are faster than PI, and EI is less complicated than GP-UCB since there are no hyper-parameters that need to be tuned in EI as in GP-UCB.…”

Section: Hyper-parameter Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications

2021

View full text Add to dashboard Cite

Controlling the water quality of water supplies has always been a critical challenge, and water resource monitoring has become a need in recent years. Manual monitoring is not recommended in the case of large water surfaces for a variety of reasons, including expense and time consumption. In the last few years, researchers have proposed the use of autonomous vehicles for monitoring tasks. Fleets or swarms of vehicles can be deployed to conduct water resource explorations by using path planning techniques to guide the movements of each vehicle. The main idea of this work is the development of a monitoring system for Ypacarai Lake, where a fleet of autonomous surface vehicles will be guided by an improved particle swarm optimization based on the Gaussian process as a surrogate model. The purpose of using the surrogate model is to model water quality parameter behavior and to guide the movements of the vehicles toward areas where samples have not yet been collected; these areas are considered areas with high uncertainty or unexplored areas and areas with high contamination levels of the lake. The results show that the proposed approach, namely the enhanced GP-based PSO, balances appropriately the exploration and exploitation of the surface of Ypacarai Lake. In addition, the proposed approach has been compared with other techniques like the original particle swarm optimization and the particle swarm optimization with Gaussian process uncertainty component in a simulated Ypacarai Lake environment. The obtained results demonstrate the superiority of the proposed enhanced GP-based PSO in terms of mean square error with respect to the other techniques.

show abstract

Section: Psomentioning

confidence: 99%

Section: Hyper-parameter Optimizationmentioning

confidence: 99%

An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications

2021

View full text Add to dashboard Cite

show abstract

“…The hyperparameter tuning of machine learning models could be regarded as an optimization process of black box functions [29]. For computational reasons, the cost of optimizing this function was high, and more importantly, the expression of the optimized function was unknown.…”

Section: Hyperparameter Optimizationmentioning

confidence: 99%

XGB+FM for Severe Convection Forecast and Factor Selection

Ding

et al. 2021

Electronics

View full text Add to dashboard Cite

In the field of meteorology, radiosonde data and observation data are critical for analyzing regional meteorological characteristics. Because of the high false alarm rate, severe convection forecasting is still challenging. In addition, the existing methods are difficult to use to capture the interaction of meteorological factors at the same time. In this research, a cascade of extreme gradient boosting (XGBoost) for feature transformation and a factorization machine (FM) for second-order feature interaction to capture the nonlinear interaction—XGB+FM—is proposed. An attention-based bidirectional long short-term memory (Att-Bi-LSTM) network is proposed to impute the missing data of meteorological observation stations. The problem of class imbalance is resolved by the support vector machines–synthetic minority oversampling technique (SVM-SMOTE), in which two oversampling strategies based on the support vector discrimination mechanism are proposed. It is proven that the method is effective, and the threat score (TS) is 7.27~14.28% higher than other methods. Moreover, we propose the meteorological factor selection method based on XGB+FM and improve the forecast accuracy, which is one of our contributions, as well as the forecast system.

show abstract

“…In the early days, attempts were made to translate human problem-solving logic into computer language. As artificial intelligence draws attention, machine learning and deep learning, which are core technologies, have also emerged as important keywords [7][8][9].…”

Section: Artificial Intelligencementioning

confidence: 99%

Affective Computing Among Individuals in Deep Learning

Kim¹

2020

J Multimed Inf Syst

View full text Add to dashboard Cite

This paper is a study of deep learning among artificial intelligence technology which has been developing many technologies recently. Especially, I am talking about emotional computing that has been mentioned a lot recently during deep learning. Emotional computing, in other words, is a passive concept that is dominated by people who scientifically analyze human sensibilities and reflect them in product development or system design, and a more active concept that studies how devices and systems understand humans and communicate with people in different modes. This emotional signal extraction, sensitivity, and psychology recognition technology is defined as a technology to process, analyze, and recognize psycho-sensitivity based on micro-small, hyper-sensor technology, and sensitive signals and information that can be sensed by the active movement of the autonomic nervous system caused by human emotional changes in everyday life. Chapter 1 talks about overview and Chapter 2 shows related research. Chapter 3 shows the problems and models of real emotional computing and Chapter 4 shows this paper as a conclusion.

show abstract

An Approach to Hyperparameter Optimization for the Objective Function in Machine Learning

Cited by 17 publications

References 14 publications

An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications

An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications

XGB+FM for Severe Convection Forecast and Factor Selection

Affective Computing Among Individuals in Deep Learning

Contact Info

Product

Resources

About