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首页> 外文会议>2011 IEEE International Conference on Systems, Man, and Cybernetics >Feature and instance selection via cooperative PSO
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Feature and instance selection via cooperative PSO

机译：通过协作PSO选择功能和实例

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摘要

Advances in data collection and storage capabilities during the past decades have led to an information overload in most application domains. The huge amount of data the real-world applications has necessitated the use of a reduction mechanism. The reduction method contains two main techniques: feature selection and instance selection, which are usually applied individually. Although, some work has been done to implement the feature and instance selection simultaneously, this work has focused on mainly the classification problem. This paper proposes the integration of feature selection and instance selection for solving the regression problem by using the fuzzy modeling approach. The selection of features and instances is based on the cooperative particle swarm optimization technique, which aims to limit the effect of the curse of dimensionality that occurs when dealing with the high dimensionality of the search space. The proposed method is applied to three real-world datasets from the machine learning repository. The algorithm's performance is illustrated by the corresponding plots of the prediction error for the different amounts of data being selected.
机译：在过去的几十年中，数据收集和存储功能的进步导致大多数应用程序域中的信息过载。现实世界中的应用程序需要处理大量数据，因此必须使用缩减机制。约简方法包含两种主要技术：特征选择和实例选择，通常通常单独应用。尽管已经完成了一些工作，以同时实现功能和实例选择，但这项工作主要集中在分类问题上。本文提出了特征选择和实例选择的集成，以使用模糊建模方法来解决回归问题。特征和实例的选择基于协作粒子群优化技术，该技术旨在限制处理高维搜索空间时发生的维数诅咒的影响。所提出的方法被应用于来自机器学习存储库的三个真实世界的数据集。对于选择的不同数据量，通过相应的预测误差图来说明算法的性能。

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