一种改进的加权特征提取算法

2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009) Pub Date : 2009-07-11 DOI:10.1109/CASE.2009.92

Fanrong Meng, Mu Zhu

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引用次数: 2

摘要

在非监督数据集中，每个特征的重要性是不同的。如果对特征设置适当的权重，能够充分考虑对聚类效应的影响程度，那么聚类结果将得到改善。提出了一个特征评价函数，通过对函数的最小化得到一组特征权重向量，这是一个多目标问题。为此，采用一种快速、最优的多目标遗传算法求解该问题并获得特征权重。最后，将特征权值引入标准K-Means算法，并在UCI数据集上进行了实验，验证了该算法的有效性。

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An Improved Weighted Feature Abstracting Algorithm

In non-supervised data set, the importance of each feature is different. If the feature is setted with a proper weight, which can fully considers the lever of the influence on the cluster effect, then the clustering result will be improved. A feature evaluate function is proposed to obtain a set of feature weight vectors by minimizing the function, which is a multi-objective problem. So a fast and elitist multi-objective genetic algorithm is used to solve the problem and obtain the weight of feature. Finally, the weight of feature is introduced into the standard K-Means algorithm and the experiments on the UCI dataset show the validity of the algorithm.

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2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009)

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