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An efficient classification framework for breast cancer using hyper parameter tuned Random Decision Forest Classifier and bayesian optimization

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期68卷 102682-102682页

作者： Kumar, Pratheep P. Bai, Mary Amala, V Nair, Geetha G. Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kumaracoil Tamil Nadu India Noorul Islam Ctr Higher Educ Dept Informat Technol Kumaracoil Tamil Nadu India Women & Children Hosp Thampanoor Kerala India

Decision tree algorithm is one of the algorithm which is easily understandable and interpretable algorithm used in both training and application purpose during breast cancer prognosis. To address this problem, Random Decision Forests are proposed. In this manuscript, the breast cancer classification can be determined by combining the advantages of Feature Weight and Hyper Parameter Tuned Random Decision Forest classifier. Here the Kernel Neutrosophic C-Means Clustering is used as Feature Weight, which allocates greater weights to applicable features and smaller weights to less applicable features. Then Random Decision Forest classifier model are optimized with the help of the bayesian optimization algorithm to obtain optimal hyper tuning parameters. By this, the accurate classification of breast cancer is successfully achieved. Then the efficiency of the proposed system is executed in python. The performance analysis are executed in Wisconsin prognostic Breast Cancer (WPBC) dataset, 70 % training and remaining 30 % testing is compared with the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the accuracy analysis of proposed feature weight and Random Decision Forest Classifier with bayesian optimization (FW + BOA-RDF) in Breast Cancer Wisconsin (Prognosis) Data Set is 6.66 %, 12.659 % and 37.618 % higher than existing method like FW + ALO-BPNN, FW + SSA-SVM, FW + GA-SVM respectively. The performance analysis in Wisconsin prognostic Breast Cancer (WPBC) dataset, 75 % training and the remaining 25 % testing is compared at Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the accuracy analysis of FW + BOA-RDF in Breast Cancer Wisconsin (Prognosis) Data Set is 3.7146 %, 5.27398 % and 4.4413 % higher than existing method like FW + ALO-BPNN, FW + SSA-SVM, FW + GA-SVM respectively.

关键词： Breast cancer Feature weighting Random Decision Forest Classifier Kernel neutrosophic C-Means clustering bayesian optimization algorithm

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Towards carbon Neutrality: Prediction of wave energy based on improved GRU in Maritime transportation

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APPLIED ENERGY 2023年 331卷

作者： Lv, Zhihan Wang, Nana Lou, Ranran Tian, Yajun Guizani, Mohsen Chinese Acad Sci Qingdao Inst Bioenergy & Bioproc Technol Extended Energy Big Data & Strategy Res Ctr Qingdao 266101 Peoples R China Uppsala Univ Fac Arts Dept Game Design S-75375 Uppsala Sweden State Key Lab Media Convergence Prod Technol & Sys Beijing 100803 Peoples R China Shandong Energy Inst Qingdao 266101 Peoples R China Qingdao New Energy Shandong Lab Qingdao 266101 Peoples R China Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Abu Dhabi U Arab Emirates

Efficient use of renewable energy is one of the critical measures to achieve carbon neutrality. Countries have introduced policies to put carbon neutrality on the agenda to achieve relatively zero emissions of greenhouse gases and to cope with the crisis brought about by global warming. This work analyzes the wave energy with high energy density and wide distribution based on understanding of various renewable energy sources. This study provides a wave energy prediction model for energy harvesting. At the same time, the Gated Recurrent Unit network (GRU), bayesian optimization algorithm, and attention mechanism are introduced to improve the model's performance. bayesian optimization methods are used to optimize hyperparameters throughout the model training, and attention mechanisms are used to assign different weights to features to increase the prediction accuracy. Finally, the 1-hour and 6-hour forecasts are made using the data from China's NJI and BSG observatories, and the system performance is analyzed. The results show that, compared with mainstream prediction algorithms, GRU based on bayesian optimization and attention mechanism has the highest prediction accuracy, with the lowest MAE of 0.3686 and 0.8204, and the highest R2 of 0.9127 and 0.6436, respectively. Therefore, the prediction model proposed here can provide support and reference for the navigation of ships powered by wave energy.

关键词： Carbon Neutrality Maritime Transportation Wave Energy Prediction bayesian optimization algorithm Gated Recurrent Unit Network

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A new prediction model based on deep learning for pig house environment

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Wu, Zhidong Xu, Kaixiang Chen, Yanwei Liu, Yonglan Song, Wusheng Qiqihar Univ Sch Mech & Elect Engn Qiqihar 161006 Peoples R China Engn Technol Res Ctr Precis Mfg Equipment & Ind Pe Qiqihar 161006 Peoples R China Heilongjiang Acad Agr Sci Harbin 150000 Peoples R China

A prediction model of the pig house environment based on bayesian optimization (BO), squeeze and excitation block (SE), convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed to improve the prediction accuracy and animal welfare and take control measures in advance. To ensure the optimal model configuration, the model uses a BO algorithm to fine-tune hyper-parameters, such as the number of GRUs, initial learning rate and L2 normal form regularization factor. The environmental data are fed into the SE-CNN block, which extracts the local features of the data through convolutional operations. The SE block further learns the weights of the feature channels, highlights the important features and suppresses the unimportant ones, improving the feature discrimination ability. The extracted local features are fed into the GRU network to capture the long-term dependency in the sequence, and this information is used to predict future values. The indoor environmental parameters of the pig house are predicted. The prediction performance is evaluated through comparative experiments. The model outperforms other models (e.g., CNN-LSTM, CNN-BiLSTM and CNN-GRU) in predicting temperature, humidity, CO2 and NH3 concentrations. It has higher coefficient of determination (R2), lower mean absolute error (MSE), and mean absolute percentage error (MAPE), especially in the prediction of ammonia, which reaches R2 of 0. 9883, MSE of 0.03243, and MAPE of 0.01536. These data demonstrate the significant advantages of the BO-SE-CNN-GRU model in prediction accuracy and stability. This model provides decision support for environmental control of pig houses.

关键词： Pig house bayesian optimization algorithm Convolutional neural network Gated recurrent unit Squeeze and excitation Environmental prediction model

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A data-driven model for predicting the mixed-mode stress intensity factors of a crack in composites

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ENGINEERING FRACTURE MECHANICS 2023年第1期288卷

作者： Zhang, Xinyu Zhao, Tingting Liu, Yifan Chen, Qingqing Wang, Zhiyong Wang, Zhihua Taiyuan Univ Technol Inst Appl Mech Coll Mech & Vehicle Engn Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Technol Shanxi Key Lab Mat Strength & Struct Impact Taiyuan 030024 Shanxi Peoples R China Beijing Inst Technol State Key Lab Explos Sci & Technol Beijing 100081 Peoples R China

A data-driven model is trained to predict mixed-mode stress intensity factors (SIFs) of composites through an artificial neural network (ANN) method. The model is based on a database generated by combining the interaction integral and the extended finite element method (XFEM). To reduce the input dimensionality and improve predictive performance, feature engineering is performed on the input data, and principal component analysis is conducted. Hyperparameters of the model are adjusted by using K-fold cross-validation and bayesian optimization algorithm (BOA) to enhance the adaptability and generalization of the model. To overcome the data scarcity issue, an active learning knowledge extraction framework is constructed, which allows for accurate knowledge extraction even with limited data. By utilizing data-driven models to solve mixed -mode SIFs, the computational cost and complexity are greatly reduced compared to numerical simulations, while computational stability and ability to deal with high-dimensional nonlinear problems are significantly improved.

关键词： Stress intensity factors Composites Data driven Active learning bayesian optimization algorithm

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State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles

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ENERGY 2023年第PartE期263卷

作者： Li, Guanzheng Li, Bin Li, Chao Wang, Shuai Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China Tianjin Univ Natl Ind Educ Platform Energy Storage Tianjin Peoples R China

Lithium-ion batteries are playing an increasingly important role in industrial applications such as electrical vehicles and energy storage systems. Their working performance and operation safety are significantly impacted by state of health (SOH), which will decrease after cycles of charging and discharging. This paper has proposed a novel two-stage SOH estimation method that can realize SOH estimation flexibly, rapidly and robustly. In the first stage, eight typical 300-s voltage profiles are used for describing the whole charging process and multiple aging features are extracted. Then, a novel stacking ensemble model with five base models is proposed. In the second stage, a Shapley additive explanation approach is introduced to obtain the contributions of features and understand why a prediction is made, thus reducing the concern of applying black-box model. The performance of the proposed model is verified using two different battery degradation datasets and the results show that the accuracy of the proposed model is better than conventional machine learning models including lightGBM, XGBoost, RF, SVR, and GPR. In addition, with various forms of noise interference, the proposed stacking model is proved to be more robust than conventional machine learning models.

关键词： Stacking ensemble model Interpretable machine learning Short-term voltage profile State of health bayesian optimization algorithm Battery aging

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An Evolutionary algorithm Taking Account of Epistasis among Parameters for Black-Box Discrete optimization

An Evolutionary Algorithm Taking Account of Epistasis among ...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Shimazu, Sho Ono, Isao Tokyo Inst Technol Sch Comp Yokohama Kanagawa Japan

ISBN: (纸本)9781728183923

We propose an evolutionary algorithm that takes account of epistasis among parameters for black-box discrete optimization problems. The black-box discrete optimization (BB-DO) is an important problem that appears in various real-world problems such as hyper-parameter optimization of machine learning and is a difficult class of optimization problems to which optimization methods that require derivative of an objective function cannot be applied. In addition, epistasis among parameters, or the dependencies among variables, makes BB-DO problems more difficult. The bayesian optimization algorithm (BOA) has been proposed as a promising method to address epistasis in BB-DO problems. However, BOA suffers from a serious problem. The problem is that the diversity of a population is likely to be lost. Therefore, BOA requires a large population size for optimization. In order to remedy the problem of BOA, we introduce three schemes to maintain the diversity of the population into BOA. In experiments, we use two benchmark problems, a 3-deceptive function and a NK-landscape, and a structural optimization of neural networks to show the effectiveness of the proposed method. The experimental results showed that the proposed method improved the number of evaluations by 31.5% and the population size by 96.9% in a 180-dimensional 3-deceptive function and found comparable or better solutions in all NK-landscape settings compared to BOA. In the structural optimization of neural networks, the proposed method improved the number of evaluations by 21.3% and the population size by 92.3% compared to BOA. In addition, the proposed method was superior to conventional optimization methods used in this field, ASNG-NAS, regularized evolution, reinforcement learning, TPE, and random search, in terms of the evaluation value.

关键词： Black-box discrete optimization Epistasis among parameters bayesian optimization algorithm Neural architecture search

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Exploiting Bivariate Dependencies to Speedup Structure Learning in bayesian optimization algorithm

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Journal of Computer Science & Technology 2012年第5期27卷 1077-1090页

作者： Amin Nikanjam Adel Rahmani School of Computer Engineering Iran University of Science and Technology

bayesian optimization algorithm （BOA） is one of the successful and widely used estimation of distribution algorithms （EDAs） which have been employed to solve different optimization problems. In EDAs, a model is learned from the selected population that encodes interactions among problem variables. New individuals are generated by sampling the model and incorporated into the population. Different probabilistic models have been used in EDAs to learn interactions. bayesian network （BN） is a well-known graphical model which is used in BOA. Learning a propel model in EDAs and particularly in BOA is distinguished as a computationally expensive task. Different methods have been proposed in the literature to improve the complexity of model building in EDAs. This paper employs bivariate dependencies to learn accurate BNs in BOA efficiently. The proposed approach extracts the bivariate dependencies using an appropriate pairwise interaction-detection metric. Due to the static structure of the underlying problems, these dependencies are used in each generation of BOA to learn an accurate network. By using this approach, the computational cost of model building is reduced dramatically. Various optimization problems are selected to be solved by the algorithm. The experimental results show that the proposed approach successfully finds the optimum in problems with different types of interactions efficiently. Significant speedups are observed in the model building procedure as well.

关键词： evolutionary computation bayesian optimization algorithm bayesian network model building bivariate interaction

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Modeling and Sectional Compensation of Temperature Error of FOG Based on BOA-GBDT 33

Modeling and Sectional Compensation of Temperature Error of ...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Cao Feng Yan Ting-yang Su Min Qilu Inst Technol Sch Mech & Elect Engn Jinan 250200 Shangdong Peoples R China

ISBN: (纸本)9781665440899

According to the requirements of temperature compensation for real-time and accuracy, a method of using bayesian algorithm to optimize the gradient boosting tree regression is proposed to establish the temperature error compensation model of fiber optic gyroscope, and it adopts the method of real-time acquisition of temperature change rate with multiple data windows to meet the requirements of online compensation and model input. The fiber optic gyroscope is placed in a temperature box to perform a temperature change test of -40-60 degrees C to obtain measured data. The temperature and temperature change rate are used as input, and bayesian algorithm optimization gradient lifting tree regression modeling and temperature rising and falling segment modeling are performed respectively. The comparative experiment results show that the proposed model achieves the best compensation effect. Through the compensation comparison test, it is verified that the proposed model has good compensation ability and generalization ability for non-training data.

关键词： Fiber optic gyroscope Temperature error bayesian optimization algorithm Gradient lifting tree Sectional compensation

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Multivariate Short Term Load Forecasting Strategy: Application to Anomalous Days of ISO New England Data 13th

Multivariate Short Term Load Forecasting Strategy: Applicati...

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13th International Joint Conference on Computational Intelligence (IJCCI) / 13th International Conference on Evolutionary Computation Theory and Applications (ECTA)

作者： Duma, Innocent Sizo Twala, Bhekisipho Durban Univ Technol Fac Engn & Built Environm Dept Elect Power Engn POB 1334 ZA-4000 Durban South Africa Durban Univ Technol Fac Engn & Built Environm POB 1334 ZA-4000 Durban South Africa

ISBN: (纸本)9789897585340

In this paper, we consider short-term electricity load forecasting which is for making forecasting within 1 hour to 7 days or a month ahead usually used for the day-to-day operations of the utility industry, such as scheduling the generation and transmission of electric energy. This is a three step process: (1) Data preprocessing which include feature extraction, (2) Modeling and (3) Model Evaluation. Electrical load time series are non stationary and notoriously very noisy because of variety of factors that affect the electrical markets. As a data preprocessing step to remove the white noise on the multivariate predictor variables (which include historical load, weather, and holidays) we perform a multivariate denoising using \ vavelets and principal component analysis (MWPCA). In the modeling step we propose three multivariate bayesian optimization (BO) based Random Forest (RF), Feedforward Neural Networks (FFNN) and Long Short-term Memory (LSTM) neural network for day ahead hourly load forecast of the anomalous days system load of the ISO New England grid. For model evaluation we used three evaluation metrics, the Mean Absolute Percent Error (MAPS), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). All the trained models achieved a superior results on the chosen model evaluation metrics most notably achieving a MAPE of less than 1% on the data under study. And the FFNN model outperformed both the RF and LSTM models.

关键词： Short Term Load Forecasting Multivariate Denoising using Wavelet and Principal Component Analysis bayesian optimization algorithm Long Short-Term Memory Neural Networks Feedforward Neural Networks Random Forest

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AN ENTROPY APPROACH TO EVALUATION RELAXATION FOR bayesian optimization algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第9期8卷 6371-6388页

作者： Hai Thi Thanh Nguyen Hoang Ngoc Luong Ahn, Chang Wook Sungkyunkwan Univ Dept Comp Engn Suwon 440746 South Korea Ctr Wiskunde & Informat NL-1090 GB Amsterdam Netherlands

bayesian optimization algorithm (BOA), a multivariate estimation of distribution algorithm, needs incorporating with efficiency enhancement techniques to be capable of solving difficult large-scale problems in a reliable and scalable manner. In this paper, we present a novel evaluation relaxation method which is based on the conditional entropy measurement. The concept of conditional entropy is rigorously analyzed and then is used to investigate the stability of the population. Especially, we utilize the evaluation relaxation strategy (ERS) proposed herein to determine whether a candidate solution should be evaluated by actual functions or be estimated by surrogate models. BOA coupled with our entropy-based ERS, termed en-BOA, shows its superiority in significantly reducing the total number of expensive fitness evaluations until reliable convergence. Experimental results prove that the entropy-based ERS enhances the efficiency of BOA while not negatively affecting the scalability of the original algorithm. In addition, the effects of our efficiency enhancement technique on population sizing requirements are also discussed.

关键词： bayesian optimization algorithm Conditional entropy Efficiency enhancement Evaluation relaxation Fitness evaluation

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