基于可变邻域搜索的分区:一种生物启发的方法

María Beatríz Bernábe Loranca, Rogelio González Velázquez, E. O. Benítez, David Pinto, J. R. Rodríguez, José Luis Martínez Flores
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引用次数: 1

摘要

人工视觉使我们能够通过遵循生命系统智能研究的技术来减少问题。一个众所周知的技术是数据挖掘和模式识别,这是依赖于人工智能的学科,从一些数据中获取知识,特别是在数据挖掘中,在生物信息学领域已经发现了一个很大的应用。更重要的是,与生物行为相关的问题产生的数据量的大量和多样化的扩展产生了构建精确的预测和分类算法的必要性。分类算法的精度会受到多种因素的影响,其中一些因素在任何自动学习算法中都被认为是通用的,因此适用于不同的研究领域。这些因素在自动学习和模式识别领域受到了关注,其中观察到不同的聚类算法,特别是自动分类或更好地称为分区分类。在这个场景中,重要的是发现一个类比,一些生物形成群体,在他们的环境中生存,找到一个最佳的序列或结构,或者将他们的物体或财产分组,反对通过分区算法进行分类。划分是一个np困难问题,因此结合近似方法是必要的。我们在这里展示的启发式方法是可变邻域搜索(VNS)这种启发式方法的重点是通过邻域来搜索邻域条件以获得满意的解决方案,就像一些生物在试图适应邻近的邻域或当前空间时通常会做的那样。在这项工作中,我们专注于以一种生物启发的方式描述一种数据挖掘技术,即包含VNS的分区分组,目的是为聚类问题找到近似的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Partitioning with Variable Neighborhood Search: A bioinspired approach
The artificial vision allows us to reduce a problem by means of techniques that have obeyed the study of the intelligence of living systems. A well-known technique is data mining and pattern recognition, which are disciplines dependent of artificial intelligence that from some data, allow the acquisition of knowledge and in particular, within data mining, a great application in the field of bioinformatics has been found. What is more, the big and diverse expansion of the amount of data produced by problems related to biological behavior has generated the necessity of constructing precise algorithms of prediction and classification. The precision of classification algorithms can be affected by diverse factors, some of them considered generics in any automatic learning algorithm and, therefore, applicable to the distinct research areas. These factors are the ones that have received attention in the field of automatic learning and pattern recognition, where different clustering algorithms are observed, in particular the automatic classification or better known as classification by partitions. In this scenery, is important to discover an analogy about the way that some living beings form groups to survive in their environment finding an optimal sequence or structure or, that group their objects or belongings, against a classification by partitions algorithm. The partitioning is an NP-hard problem, thus the incorporation of approximated methods is necessary. The heuristic that we expose here is Variable Neighborhood Search (VNS) focusing in the way that this heuristic does the search of neighbor conditions by means of neighborhoods to get a satisfactory solution, just like some living beings usually do it when they try to adapt to a neighborhood close to theirs or to the current space. In this work, we focus on describing in a bioinspired way, a technique of data mining known as partitional grouping with the inclusion of VNS with the purpose of finding approximated solutions for a clustering problem.
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