Abstract: With excellent classification effect and solid theoretical foundation,support vector machines have become one of the most important classification method in the field of pattern recognition,machine learning and data mining in recent years.How-ever,their training time becomes much longer with the increase of training instances.In the case of multi-class classification,the training process will become even more complex.To deal with above problems,a fast data reduction method named as MOIS is proposed for multi-class classification.With cluster centers being used as reference points,redundent instances can be deleted,bound instances crucial for the trainning can be selected,and the distribution imbalance between classes can also be relieved by the proposed method.Experiments show that MOIS can enormously improve the training efficiency while keeping or even improving the classification accuracy.For example,on Optdigit dataset,the classification accuracy is increased from 98.94% to 99.05%,while the training time is reduced to 0.15% of the original.What's more,on the dataset formed by the first 100 classes of HCL2000,the training time of the proposed method is reduced to less than 6% of original,while the accuracy is improved slightly from 99.29% to 99.30%.Furthermore,MIOS is highly efficient.

Key words: Clustering, Data reduction, Instance seletion, Multi-class classification, Support vector machines