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An unsupervised attribute clustering algorithm for unsupervised feature selection

机译：用于无监督特征选择的无监督属性聚类算法

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摘要

The curse of dimensionality refers to the problem that one faces when analyzing datasets with thousands or hundreds of thousands of attributes. This problem is usually tackled by different feature selection methods which have been shown to effectively reduce computation time, improve prediction performance, and facilitate better understanding of datasets in various application areas. These methods can be classified into filter methods, wrapper methods and embedded methods. All of these feature selection methods require class label information to perform their tasks. Hence, when such information is unavailable, the feature selection problem can be very challenging. In order to overcome the above challenges, we propose an unsupervised feature selection method which is called Unsupervised Attribute Clustering Algorithm (UACA) involved in several steps: i) calculate the value of Maximal Information Coefficient for each pair of attributes to construct an attributes distance matrix; ii) cluster all attributes using optimal k-mode clustering method to find out k modes attributes as features of each cluster. For evaluating the performance of the proposed algorithm, classification problems with different classifiers were tested to validate the method and compare with other methods. The results of data experiments exhibit the proposed unsupervised algorithm which is comparable with classical feature selection methods and even outperforms some supervised learning algorithm.
机译：维度诅咒是指人们在分析具有成千上万个属性的数据集时面临的问题。通常通过不同的特征选择方法来解决此问题，这些特征选择方法已被证明可以有效地减少计算时间，提高预测性能并有助于更好地理解各个应用领域中的数据集。这些方法可以分为过滤方法，包装方法和嵌入方法。所有这些功能选择方法都需要类标签信息来执行其任务。因此，当此类信息不可用时，特征选择问题可能会非常具有挑战性。为了克服上述挑战，我们提出了一种无监督的特征选择方法，称为无监督属性聚类算法（UACA），涉及以下几个步骤：i）计算每对属性的最大信息系数的值，以构建属性距离矩阵; ii）使用最佳k模式聚类方法对所有属性进行聚类，以找出k个模式属性作为每个聚类的特征。为了评估所提出算法的性能，测试了具有不同分类器的分类问题，以验证该方法并与其他方法进行比较。数据实验结果表明，提出的无监督算法可与经典特征选择方法相提并论，甚至优于某些有监督的学习算法。

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