Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning m...Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.展开更多
针对包含复杂约束条件的约束多目标优化问题(CMOP),在确保算法满足严格约束的同时,有效平衡算法的收敛性与多样性是重大挑战。因此,提出一种双种群双阶段的进化算法(DPDSEA)。该算法引入2个独立进化种群:主种群和副种群,并分别利用可行...针对包含复杂约束条件的约束多目标优化问题(CMOP),在确保算法满足严格约束的同时,有效平衡算法的收敛性与多样性是重大挑战。因此,提出一种双种群双阶段的进化算法(DPDSEA)。该算法引入2个独立进化种群:主种群和副种群,并分别利用可行性规则和改进的epsilon约束处理方法进行更新。在第一阶段,主种群和副种群分别探索约束Pareto前沿(CPF)与无约束Pareto前沿(UPF),从而获取UPF和CPF的位置信息;在第二阶段,设计一种分类方法,根据UPF与CPF的位置对CMOP进行分类,从而对不同类型的CMOP执行特定的进化策略;此外,提出一种随机扰动策略,在副种群进化到CPF附近时,对它进行随机扰动以产生一些位于CPF上的个体,从而促进主种群在CPF上的收敛与分布。把所提算法与6个具有代表性的算法:CMOES(Constrained Multi-objective Optimization based on Even Search)、dp-ACS(dual-population evolutionary algorithm based on Adaptive Constraint Strength)、c-DPEA(DualPopulation based Evolutionary Algorithm for constrained multi-objective optimization)、CAEAD(Constrained Evolutionary Algorithm based on Alternative Evolution and Degeneration)、BiCo(evolutionary algorithm with Bidirectional Coevolution)和DDCMOEA(Dual-stage Dual-population Evolutionary Algorithm for Constrained Multiobjective Optimization)在LIRCMOP和DASCMOP两个测试集上进行实验比较。实验结果表明,DPDSEA在23个问题中取得了15个最优反转世代距离(IGD)值和12个最优超体积(HV)值,展现了DPDSEA在处理复杂CMOP时显著的性能优势。展开更多
基金supported by Research Grants Council of Hong Kong under Grant No.17301214HKU CERG Grants,Fundamental Research Funds for the Central Universities+2 种基金the Research Funds of Renmin University of ChinaHung Hing Ying Physical Research Grantthe Natural Science Foundation of China under Grant No.11271144
文摘Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.
文摘针对包含复杂约束条件的约束多目标优化问题(CMOP),在确保算法满足严格约束的同时,有效平衡算法的收敛性与多样性是重大挑战。因此,提出一种双种群双阶段的进化算法(DPDSEA)。该算法引入2个独立进化种群:主种群和副种群,并分别利用可行性规则和改进的epsilon约束处理方法进行更新。在第一阶段,主种群和副种群分别探索约束Pareto前沿(CPF)与无约束Pareto前沿(UPF),从而获取UPF和CPF的位置信息;在第二阶段,设计一种分类方法,根据UPF与CPF的位置对CMOP进行分类,从而对不同类型的CMOP执行特定的进化策略;此外,提出一种随机扰动策略,在副种群进化到CPF附近时,对它进行随机扰动以产生一些位于CPF上的个体,从而促进主种群在CPF上的收敛与分布。把所提算法与6个具有代表性的算法:CMOES(Constrained Multi-objective Optimization based on Even Search)、dp-ACS(dual-population evolutionary algorithm based on Adaptive Constraint Strength)、c-DPEA(DualPopulation based Evolutionary Algorithm for constrained multi-objective optimization)、CAEAD(Constrained Evolutionary Algorithm based on Alternative Evolution and Degeneration)、BiCo(evolutionary algorithm with Bidirectional Coevolution)和DDCMOEA(Dual-stage Dual-population Evolutionary Algorithm for Constrained Multiobjective Optimization)在LIRCMOP和DASCMOP两个测试集上进行实验比较。实验结果表明,DPDSEA在23个问题中取得了15个最优反转世代距离(IGD)值和12个最优超体积(HV)值,展现了DPDSEA在处理复杂CMOP时显著的性能优势。
