一种改进的k-近邻文本分类算法
An Improved k-Nearest Neighbor Algorithm for Text Categorization
摘要：
k是基于k-近邻算法（kNN）的文本分类系统中最重要的参数，在分类过程中，首先确定训练集中与测试文档最近的k个文档。然后，可以根据这k个最近邻的类别分布进行预测。一般来说，训练集中的类分布是不均匀的。某些类可能比其他类具有更多的样本。因此，系统性能对参数k的选择非常敏感。固定的k值很可能会导致对大类别的偏见。为了解决这些问题，我们提出了一种改进的kNN算法，它使用不同数量的最近邻为不同的类别，而不是一个固定的数量在所有类别。更多的样本（最近邻）将用于决定是否将测试文档分类到训练集中具有更多样本的类别。在中文文本分类上的初步实验表明，该方法对参数k的敏感性比传统方法低，并且在k较大的情况下也能正确地对较小类别的文档进行分类。该方法是有前途的某些情况下，估计参数k通过交叉验证是不允许的。
英文摘要：
k is the most important parameter in a text categorization system based on k-Nearest Neighbor algorithm (kNN).In the classification process, k nearest documents to the test one in the training set are determined firstly. Then, the predication can be made according to the category distribution among these k nearest neighbors. Generally speaking, the class distribution in the training set is uneven. Some classes may have more samples than others. Therefore, the system performance is very sensitive to the choice of the parameter k. And it is very likely that a fixed k value will result in a bias on large categories. To deal with these problems, we propose an improved kNN algorithm, which uses different numbers of nearest neighbors for different categories, rather than a fixed number across all categories. More samples (nearest neighbors) will be used for deciding whether a test document should be classified to a category, which has more samples in the training set. Preliminary experiments on Chinese text categorization show that our method is less sensitive to the parameter k than the traditional one, and it can properly classify documents belonging to smaller classes with a large k. The method is promising for some cases, where estimating the parameter k via cross-validation is not allowed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
