Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
复制标题
数据库知识发现的多目标进化算法
DOI：
10.1007/978-3-540-77467-9
复制
发表时间：
2008
影响因子：
1.8
通讯作者：
Susmita K. Ghosh
中科院分区：
文献类型：
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作者：
Ashish Ghosh;Satchidananda Dehuri;Susmita K. Ghosh
数据挖掘（DM）是描述这种数据计算分析的最常用名称，所获得的结果必须符合几个目标，如准确性、可理解性、用户兴趣等。尽管各个跨学科领域开发了许多复杂的技术，但只有少数技术能够很好地处理多标准决策问题。因此，多目标进化算法（MOEA）对于数据库知识发现（KDD）中数据挖掘任务的重要性，已经引起了建立良好的多目标遗传算法社区的广泛关注，以优化多目标遗传算法任务中的目标。本卷提供了七篇文章的集合，其中包含了新的和高质量的研究成果。这些文章是由世界各地的顶尖专家撰写的。展示了如何以单独和集成的方式利用不同的moea，以各种方式有效地从大型数据库中挖掘数据。
Data Mining (DM) is the most commonly used name to describe such computational analysis of data and the results obtained must conform to several objectives such as accuracy, comprehensibility, interest for the user etc. Though there are many sophisticated techniques developed by various interdisciplinary fields only a few of them are well equipped to handle these multi-criteria issues of DM. Therefore, the DM issues have attracted considerable attention of the well established multiobjective genetic algorithm community to optimize the objectives in the tasks of DM. The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.