检索结果-南通市图书馆

hyper-parameter optimization of multiple machine learning algorithms for molecular property prediction using hyperopt library

引用

Chinese Journal of Chemical Engineering 2022年第12期52卷 115-125页

作者： Jun Zhang Qin Wang Weifeng Shen School of Chemistry and Chemical Engineering Chongqing UniversityChongqing 401331China School of Chemistry and Chemical Engineering Chongqing University of Science&TechnologyChongqing 401331China Chongqing Key Laboratory of Theoretical and Computational Chemistry Chongqing 400044China

Due to outstanding performance in cheminformatics,machine learning algorithms have been increasingly used to mine molecular properties and biomedical big data.The performance of machine learning models is known to critically depend on the selection of the hyper-parameter configuration.However,many studies either explored the optimal hyper-parameters per the grid searching method or employed arbitrarily selected hyper-parameters,which can easily lead to achieving a suboptimal hyper-parameter configuration.In this study,hyperopt library embedding with the Bayesian optimization is employed to find optimal hyper-parameters for different machine learning algorithms.Six drug discovery datasets,including solubility,probe-likeness,h ERG,Chagas disease,tuberculosis,and malaria,are used to compare different machine learning algorithms with ECFP6 fingerprints.This contribution aims to evaluate whether the Bernoulli Na?ve Bayes,logistic linear regression,Ada Boost decision tree,random forest,support vector machine,and deep neural networks algorithms with optimized hyper-parameters can offer any improvement in testing as compared with the referenced models assessed by an array of metrics including AUC,F1-score,Cohen’s kappa,Matthews correlation coefficient,recall,precision,and accuracy.Based on the rank normalized score approach,the hyperopt models achieve better or comparable performance on 33 out 36 models for different drug discovery datasets,showing significant improvement achieved by employing the hyperopt library.The open-source code of all the 6 machine learning frameworks employed in the hyperopt python package is provided to make this approach accessible to more scientists,who are not familiar with writing code.

关键词： Machine learning Prediction Optimal design hyper-parameter optimization hyperopt library

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation

引用

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2022年第12期23卷 1027-1046页

作者： Fei LV Jia YU Jun ZHANG Peng YU Da-wei TONG Bin-ping WU State Key Laboratory of Hydraulic Engineering Simulation and Safety Tianjin UniversityTianjin 300350China

Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity and overfitting.In addition,the influence of environmental and operational factors is often ignored.In response,a novel stacking-based ensemble learning method taking into account the combined effects of those factors is proposed.Through multiple comparison tests,four models,e Xtreme gradient boosting(XGBoost),random forest(RF),back propagation neural network(BPNN)as the base learners,and support vector regression(SVR)as the meta-learner,are selected for stacking.Furthermore,an improved cuckoo search optimization(ICSO)algorithm is developed for hyper-parameter optimization of the ensemble model.The application to a real-world project demonstrates that the proposed method outperforms the popular single ML method XGBoost and the ensemble model optimized by particle swarm optimization(PSO),with 16.43%and 4.88%improvements of mean absolute percentage error(MAPE),respectively.

关键词： Drilling efficiency Prediction Earth-rock excavation Stacking-based ensemble learning Improved cuckoo search optimization(ICSO)algorithm Comprehensive effects of various factors hyper-parameter optimization

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Optimized Decision Tree and Black Box Learners for Revealing Genetic Causes of Bladder Cancer

引用

Intelligent Automation & Soft Computing 2023年第7期37卷 49-71页

作者： Sait Can Yucebas Computer Engineering Department Canakkale Onsekiz Mart UniversityCanakkale17100Turkey

The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited.These studies focus on the prediction performance,and the extraction of genomic factors that cause disease is often overlooked.However,finding underlying genetic causes is very important in terms of early diagnosis,development of diagnostic kits,preventive medicine,etc.The motivation of our study was to diagnose bladder cancer(BCa)based on genetic data and to reveal underlying genetic factors by using machine-learning models.In addition,conducting hyper-parameter optimization to get the best performance from different models,which is overlooked in most studies,was another objective of the study.Within the framework of these motivations,C4.5,random forest(RF),artificial neural networks(ANN),and deep learning(DL)were used.In this way,the diagnostic performance of decision tree(DT)-based models and black box models on BCa was also compared.The most successful model,DL,yielded an area under the curve(AUC)of 0.985 and a mean square error(MSE)of 0.069.For each model,hyper-parameters were optimized by an evolutionary algorithm.On average,hyper-parameter optimization increased MSE,root mean square error(RMSE),LogLoss,and AUC by 30%,17.5%,13%,and 6.75%,respectively.The features causing BCa were extracted.For this purpose,entropy and Gini coefficients were used for DT-based methods,and the Gedeon variable importance was used for black box methods.The single nucleotide polymorphisms(SNPs)rs197412,rs2275928,rs12479919,rs798766 and rs2275928,whose BCa relations were proven in the literature,were found to be closely related to BCa.In addition,rs1994624 and rs2241766 susceptibility loci were proposed to be examined in future studies.

关键词： Random forest neural network deep learning hyper-parameter optimization bladder cancer single nucleotide polymorphism

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：