李勇明
智能计算,模式识别,机器学习,医学图像处理,数据分析等。
个性化签名
- 姓名:李勇明
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学科领域:
模式识别
- 研究兴趣:智能计算,模式识别,机器学习,医学图像处理,数据分析等。
李勇明,博士,副教授,硕士生导师,现在重庆大学通信工程学院工作。1999年,在电子科技大学获通信工程学士学位; 2003年,在重庆大学通信工程学院获电路与系统硕士学位;2007年,在重庆大学通信工程学院获电路与系统博士学位;2008.12-2010.1 美国宾夕法尼亚州立大学计算机学院访问学者;2009.2-2009.12 美国卡内基梅隆大学计算机学院博士后研究。 目前研究方向为:智能计算、模式识别、机器学习、医学图像处理、数据分析等。 现为ACM会员、IEEE会员、中国计算机学会会员、中国通信学会会员、中国人工智能学会会员。担任国际期刊《International Journal of Computer Applications in Technology》(EI源)、《International Journal of Swarm Intelligence Research》、《International Journal of Applied Evolutionary Computation》、《Far East Journal of Experimental and Theoretical Artificial Intelligence》、《Advances in Computer Science and Engineering》编委,担任国际期刊《Applied Intelligence》(SCI源)审委。担任国内核心期刊《生物医学工程与临床》特约编委。担任CMPB、NEUCOM、APIN、CMPIN、CAMWA、IJCAT、PATREC、CIE39、iCBBE2009、《自动化学报》、《系统仿真学报》、《计算机应用》、《计算机应用研究》、《数据采集与处理》等多个国内外核心期刊国际会议审稿人。 主持及参与国家自然科学基金、重庆市自然科学基金、重庆大学青年骨干教师创新培育基金、重庆大学生创新基金等各类项目8项。在ESWA、IJITDM、EAAI、CMPB、NEUCOM、APIN、APJOR、JOFCIS、CESA2006、ICONIP2008、软件学报等国内外期刊会议上发表及录用了论文40余篇,其中SCI收录4篇,EI收录13篇,ISTP收录论文2篇。已授权实用新型专利3项,正在受理发明专利4项。 已获得重庆大学青年教师创新奖教金(2009)、重庆市优秀博士学位论文奖(2008)、重庆大学优秀博士学位论文奖(2007)、重庆大学优秀教材二等奖(2008)。
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【期刊论文】Sequential multi-criteria feature selection algorithm based on agent genetic algorithm
李勇明
,-0001,():
-1年11月30日
A multi-criteria feature selection method-sequential multi-criteria feature selection algorithm (SMCFS) has been proposed for the applications with high precision and low time cost. By combining the consistency and otherness of different evaluation criteria, the SMCFS adopts more than one evaluation criteria sequentially to improve the efficiency of feature selection. With one novel agent genetic algorithm (chain-like agent GA), the SMCFS can obtain high precision of feature selection and low time cost that is similar as filter method with single evaluation criterion. Several groups of experiments are carried out for comparison to demonstrate the performance of SMCFS. SMCFS is compared with different feature selection methods using three datasets from UCI database. The experimental results show that the SMCFS can get low time cost and high precision of feature selection, and is very suitable for this kind of applications of feature selection
Sequential,, Multi-criteria,, Feature selection,, agent,, genetic algorithm
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李勇明
,-0001,():
-1年11月30日
For the low optimization precision and long optimization time of genetic algorithm, this paper proposed a multi-population agent co-genetic algorithm with chain-like agent structure (MPAGA). This algorithm adopted multipopulation parallel searching mode, close chain-like agent structure, cycle chain-like agent structure, dynamic neighborhood competition and orthogonal crossover strategy to realize parallel optimization, and has the characteristics of high optimization precision and short optimization time. In order to verify the optimization precision of this algorithm, some popular benchmark test functions were used for comparing this algorithm and a popular agent genetic algorithm (MAGA). The experimental results show that MPAGA has higher optimization precision and shorter optimization time than MAGA.
Genetic algorithm,, Multi-population,, Agent ,, chain-like agent structure
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李勇明
,-0001,():
-1年11月30日
In this paper, one novel genetic algorithm dynamic chain-like agent genetic algorithm (CAGA) is proposed for solving global numerical optimization problem and feature selection problem. The CAGA combines the chain-like agent structure with dynamic neighboring genetic operators to get higher optimization capability. An agent in chain-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and can use knowledge to increase energies. Global numerical optimization problem and feature selection problem are the most important problems for evolutionary algorithm, especially for genetic algorithm. Hence, the experiments of global numerical optimization and feature selection are necessary to verify the performance of genetic algorithms. Corresponding experiments have been done and show that CAGA is suitable for real coding and binary coding optimization problems, and has more precise and more stable optimization results.
Genetic algorithm,, chainlike,, numerical optimization,, feature selection
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李勇明, Yong-Ming
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 84 (2006) 162-173,-0001,():
-1年11月30日
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李勇明, Zeng
IMACS Multiconference on "Computational Engineering in Systems Applications" (CESA), October 4-6, 2006, Beijing, China,-0001,():
-1年11月30日
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