This paper presents a novel and effective classification method for imbalanced data sets.The core idea of the algorithm

which is composed of three parts

is to provide a general solution for IDS classification by both sample preprocessing and classifier improving.Firstly

we re-sample the imbalance data by using variable SOM clustering so as to overcome the flaws of the traditional re-sampling methods

such as serious randomness

subjective interference and information loss.Then we cut down the sampled data sets according to the K-NN rule to solve the problem of data confusion

which improves the generalization of SVM.Especially

in order to adapt the class imbalance

the class boundary alignment is introduced through conformal transform on kernel function.The comparison results show the effectiveness of three algorithms.Meanwhile

the algorithm has also been used in our question answer system

which obtains outstanding result in the international TREC-2006 QA track.