关键词: 邻域粗糙模型, 约简, 属性重要度, 特征贡献率, 融合特征子空间, 样本相似度

Abstract: The feature extraction is based on a reduction, and then to add several features that the value of attribute significance or contribution rate is large according to certain strategy, the feature subset space combined is constituted. With the idea of kNN, to calculate the category percentage of the k-nearest neighbors around the test sample. In order to improve the classification accuracy, the sample similarity measure is introduced to calculate the similarity between the test samples and k-nearest neighbors. By setting the appropriate threshold of the category percentage and the sample similarity, to ultimately determine the category of the test samples. The algorithm’s validity and feasibility have been verified by six multidimensional data sets from UCI. The impact of the different reductions and different thresholds for classification accuracy is analysed detailedly.

Key words: neighborhood rough set model, reduction, attribute significance, feature contribution rate, feature subspace combined, sample similarity