As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimizat...As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimization model based on BSO algorithm has been proposed,which proves its effectiveness in solving the classification problem.However,BSO algorithm also has defects.For example,large-scale datasets make the structure of the model complex,which affects its classification performance.In addition,in the process of optimization,the information of the dominant solution cannot be well preserved in BSO,which leads to its limitations in classification performance.Moreover,its generation strategy is inefficient in solving a variety of complex practical problems.Therefore,we briefly introduce the optimization model structure by feature selection.Besides,this paper retains the brainstorming process of BSO algorithm,and embeds the new generation strategy into BSO algorithm.Through the three generation methods of global optimal,local optimal and nearest neighbor,we can better retain the information of the dominant solution and improve the search efficiency.To verify the performance of the proposed generation strategy in solving the classification problem,twelve datasets are used in experiment.Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.展开更多
Multiple Sclerosis(MS)is a disease that disrupts the flow of information within the brain.It affects approximately 1 million people in the US.And remains incurable.MS treatments can cause side effects and impact the q...Multiple Sclerosis(MS)is a disease that disrupts the flow of information within the brain.It affects approximately 1 million people in the US.And remains incurable.MS treatments can cause side effects and impact the quality of life and even survival rates.Based on existing research studies,we investigate the risks and benefits of three treatment options based on methylprednisolone(a corticosteroid hormone medication)prescribed in(1)high-dose,(2)low-dose,or(3)no treatment.The study currently prescribes one treatment to all patients as it has been proven to be the most effective on average.We aim to develop a personalized approach by building machine learningmodels and testing their sensitivity against changes in the data.We first developed an unsupervised predictive-prescriptive model based on K-means clustering in addition to three predictive models.We then assessed the models’performance with patient data perturbations and finally developed a robust model by re-training on a set that includes perturbations.These increased themodels’robustness in highly perturbed scenarios(+10%accuracy)while having no cost in scenarios without perturbations.We conclude by discussing the trade-off between robustification and its interpretability cost.展开更多
基金supported by the National Natural Science Foundation of China(61876089,61403206,61876185,61902281)the opening Project of Jiangsu Key Laboratory of Data Science and Smart Software(No.2019DS302)+2 种基金the Natural Science Foundation of Jiangsu Province(BK20141005)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(14KJB520025)the Engineering Research Center of Digital Forensics,Ministry of Education,and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimization model based on BSO algorithm has been proposed,which proves its effectiveness in solving the classification problem.However,BSO algorithm also has defects.For example,large-scale datasets make the structure of the model complex,which affects its classification performance.In addition,in the process of optimization,the information of the dominant solution cannot be well preserved in BSO,which leads to its limitations in classification performance.Moreover,its generation strategy is inefficient in solving a variety of complex practical problems.Therefore,we briefly introduce the optimization model structure by feature selection.Besides,this paper retains the brainstorming process of BSO algorithm,and embeds the new generation strategy into BSO algorithm.Through the three generation methods of global optimal,local optimal and nearest neighbor,we can better retain the information of the dominant solution and improve the search efficiency.To verify the performance of the proposed generation strategy in solving the classification problem,twelve datasets are used in experiment.Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.
文摘Multiple Sclerosis(MS)is a disease that disrupts the flow of information within the brain.It affects approximately 1 million people in the US.And remains incurable.MS treatments can cause side effects and impact the quality of life and even survival rates.Based on existing research studies,we investigate the risks and benefits of three treatment options based on methylprednisolone(a corticosteroid hormone medication)prescribed in(1)high-dose,(2)low-dose,or(3)no treatment.The study currently prescribes one treatment to all patients as it has been proven to be the most effective on average.We aim to develop a personalized approach by building machine learningmodels and testing their sensitivity against changes in the data.We first developed an unsupervised predictive-prescriptive model based on K-means clustering in addition to three predictive models.We then assessed the models’performance with patient data perturbations and finally developed a robust model by re-training on a set that includes perturbations.These increased themodels’robustness in highly perturbed scenarios(+10%accuracy)while having no cost in scenarios without perturbations.We conclude by discussing the trade-off between robustification and its interpretability cost.
