Irrelevant and redundant features increase the computation and storage requirements, and the extraction of required information becomes challenging. Feature selection enables us to extract the useful information from the given data. Streaming feature selection is an emerging field for the processing of high-dimensional data, where the total number of attributes may be infinite or unknown while the number of data instances is fixed. We propose a hybrid feature selection approach for streaming features using ant colony optimization with symmetric uncertainty (ACO-SU). The proposed approach tests the usefulness of the incoming features and removes the redundant features. The algorithm updates the obtained feature set when a new feature arrives. We evaluate our approach on fourteen datasets from the UCI repository. The results show that our approach achieves better accuracy with a minimal number of features compared with the existing methods.

中文翻译：

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基于蚁群优化的流特征选择：在医学图像诊断中的应用

不相关和冗余的特征增加了计算和存储要求，并且提取所需信息变得具有挑战性。特征选择使我们能够从给定的数据中提取有用的信息。流特征选择是处理高维数据的新兴领域，其中属性的总数可能是无限的或未知的，而数据实例的数量是固定的。我们提出了一种使用具有对称不确定性的蚁群优化（ACO-SU）的流特征混合特征选择方法。所提出的方法测试传入特征的有用性并删除冗余特征。当新特征到达时，算法更新获得的特征集。我们在来自 UCI 存储库的 14 个数据集上评估我们的方法。