关键词: 互近邻, 增量学习, 数据流分类, 不确定性, 概念漂移

Abstract: Application scenarios of dynamic data streams classification are increasing, and it is very important to discriminate concept drift from noisy information in data streams classification. This paper proposes an incremental classification model with noisy elimination for dynamic data streams classification to solve this problem. When dynamic data streams sample is incompatible with the concept of classifier model, the mutual nearest neighbor is used to detect noisy sample. Based on it, support vector machine is used as learner, and then concept drift in data streams is solved by incremental learning. Under two different metrics about incompatibility, classification schema is effective through the theory analysis and simulation experiment.

Key words: mutual nearest neighbor, incremental learning, data streams classification, uncertainty, concept drift