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Data classification through an evolutionary approach based on multiple criteria

机译：通过基于多种标准的进化方法进行数据分类

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摘要

Real-world problems usually present a huge volume of imprecise data. These types of problems may challenge case-based reasoning systems because the knowledge extracted from data is used to identify analogies and solve new problems. Many authors have focused on organizing case memory in patterns to minimize the computational burden and deal with uncertainty. The organization is usually determined by a single criterion, but in some problems, a single criterion can be insufficient to find accurate clusters. This work describes an approach to organize the case memory in patterns based on multiple criteria. This new approach uses the searching capabilities of multiobjective evolutionary algorithms to build a Pareto set of solutions, where each one is a possible organization based on the relevance of objectives. The system shows promising capabilities when it is compared with a successful system based on self-organizing maps. Due to the data set geometry influences, the clustering building process results are analyzed taking into account it. For this reason, some complexity measures are used to categorize data sets according to their topology.
机译：实际问题通常会呈现大量不精确的数据。这些类型的问题可能会挑战基于案例的推理系统，因为从数据中提取的知识用于识别类比并解决新问题。许多作者集中于以模式组织案例存储，以最大程度地减少计算负担并处理不确定性。组织通常由单个标准确定，但是在某些问题中，单个标准可能不足以找到准确的集群。这项工作描述了一种基于多个条件以模式组织案例存储的方法。这种新方法利用多目标进化算法的搜索功能来构建Pareto解决方案集，其中每个解决方案都是基于目标相关性的可能组织。与基于自组织映射的成功系统进行比较时，该系统显示出令人鼓舞的功能。由于数据集几何形状的影响，因此考虑了聚类构建过程的结果进行分析。因此，一些复杂性度量用于根据数据集的拓扑对其进行分类。

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