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

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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 *** performance of machine learning models is known to critically depend on the selection of the hyper-parameter ***,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 *** this study,hyperopt library embedding with the Bayesian optimization is employed to find optimal hyper-parameters for different machine learning *** drug discovery datasets,including solubility,probe-likeness,h ERG,Chagas disease,tuberculosis,and malaria,are used to compare different machine learning algorithms with ECFP6 *** 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 *** 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 *** 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

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

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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 *** single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity and *** addition,the influence of environmental and operational factors is often *** response,a novel stacking-based ensemble learning method taking into account the combined effects of those factors is *** 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 ***,an improved cuckoo search optimization(ICSO)algorithm is developed for hyper-parameter optimization of the ensemble *** 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

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

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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 *** studies focus on the prediction performance,and the extraction of genomic factors that cause disease is often ***,finding underlying genetic causes is very important in terms of early diagnosis,development of diagnostic kits,preventive medicine,*** motivation of our study was to diagnose bladder cancer(BCa)based on genetic data and to reveal underlying genetic factors by using machine-learning *** addition,conducting hyper-parameter optimization to get the best performance from different models,which is overlooked in most studies,was another objective of the *** the framework of these motivations,C4.5,random forest(RF),artificial neural networks(ANN),and deep learning(DL)were *** this way,the diagnostic performance of decision tree(DT)-based models and black box models on BCa was also *** most successful model,DL,yielded an area under the curve(AUC)of 0.985 and a mean square error(MSE)of *** each model,hyper-parameters were optimized by an evolutionary *** average,hyper-parameter optimization increased MSE,root mean square error(RMSE),LogLoss,and AUC by 30%,17.5%,13%,and 6.75%,*** features causing BCa were *** this purpose,entropy and Gini coefficients were used for DT-based methods,and the Gedeon variable importance was used for black box *** 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 *** 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

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GA-1DLCNN method and its application in bearing fault diagnosis

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Journal of Southeast University(English Edition) 2019年第1期35卷 36-42页

作者： Yang Zhenbo Jia Minping School of Mechanical Engineering Southeast UniversityNanjing 211189China

Due to the fact that the vibration signal of the rotating machine is one-dimensional and the large-scale convolution kernel can obtain a better perception field, on the basis of the classical convolution neural network model(LetNet-5), one-dimensional large-kernel convolution neural network(1 DLCNN) is designed. Since the hyper-parameters of 1 DLCNN have a greater impact on network performance, the genetic algorithm(GA) is used to optimize the hyper-parameters, and the method of optimizing the parameters of 1 DLCNN by the genetic algorithm is named GA-1 DLCNN. The experimental results show that the optimal network model based on the GA-1 DLCNN method can achieve 99.9% fault diagnosis accuracy, which is much higher than those of other traditional fault diagnosis methods. In addition, the 1 DLCNN is compared with one-dimencional small-kernel convolution neural network(1 DSCNN) and the classical two-dimensional convolution neural network model. The input sample lengths are set to be 128, 256, 512, 1 024, and 2 048, respectively, and the final diagnostic accuracy results and the visual scatter plot show that the effect of 1 DLCNN is optimal.

关键词： one-dimensional convolution neural network large-size convolution kernel hyper-parameter optimization genetic algorithm

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