Abstract: As one of the most classical TC approaches,k-NN is advantaged in tackling concept drift. However,to avoid curse of dimensionality,it has to employ FS(feature selection) method to reduce dimensionality of feature space and improve operation efficiency. But FS process will generally cause information losing and thus has some side-effects on the whole performance of approach. According to sparsity of text vectors, an optimized k-NN approach was presented in paper. This optimized approach greatly simplified euclidean distance model and reduced the operation cost without any information losing. So it can simultaneously achieve much higher both performance and efficiency than general k-NN approach. It then enhanced the advantage of k-NN in managing concept drift.

Key words: Text categorization,Feature selection,k-NN,Concept drift