A method and system for classifying input data arrived one by one in time, is provided including: a) respectively training a group of classifiers with a predetermined number with recent or previous input data whose real classes are obtained as learning samples, wherein a number of the recent input data are increased progressively in reverse chronological order; b) selecting the classifier having the highest accuracy on the recent input data from the group of classifiers based on recent classifying results of the group of classifiers; and c) classifying current input data using the selected classifier.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for classifying input data arriving one by one in time, comprising: respectively training a group of classifiers with a predetermined number of previous input data whose real classes are obtained as learning samples, wherein the number of the previous input data are increased progressively in a reverse chronological order; selecting a classifier having a highest accuracy on the previous input data from the group of classifiers based on previous classifying results of the group of classifiers; and classifying current input data using the classifier selected. 2. The method according to claim 1, wherein the selecting further comprises: calculating a weight of each classifier in the group of classifiers based on the predetermined number of previous input data whose real classes are obtained, wherein, while the classifier gives a right class, the input data is more previous in time, a contribution thereof to the weight of the classifier is larger than for input data that is less previous in time; and selecting the classifier whose weight is a highest as the classifier having the highest accuracy on the previous input data. 3. The method according to claim 2, wherein the weight Wi of each classifier in the group of classifiers is calculated by: W i = ∑ k = 1 M   1 k  p  ( r k , l k ) wherein M represents the number predetermined of the previous input data whose real classes are obtained; wherein k represents a kth previous input data in the previous input data whose real classes are obtained, k=1, . . . M; wherein rk represents a classifying result of an ith classifier on the kth previous input data, and lk represents a real class of the kth previous input data; and wherein when the classifying result of the ith classifier on the kth previous input data is right, p(rk,lk)=1, otherwise, p(rk,lk)=0. 4. The method according to claim 1, wherein the number Si of learning samples for training each classifier in the group of classifiers with the predetermined number in the training is calculated by: Si=i*N wherein i=1, . . . C, C represents the number of the classifiers in the group of classifiers, and N represents the number of the previous input data for training the first classifier in the group of classifiers. 5. The method according to claim 3, further comprising storing the previous input data and the real classes thereof using a storage. 6. The method according to claim 4, wherein a largest number Q of the previous input data stored by the storage is calculated by: Q=C*N. 7. The method according to claim 1, wherein the training is performed after accumulating the predetermined number of previous input data whose real classes are obtained. 8. The method according to claim 1, wherein the real classes in the training are one of provided by a user and obtained automatically. 9. The method according to claim 1, wherein the classifiers in the group of classifiers are one of identical and different. 10. The method according to claim 1, wherein the classifiers in the group of classifiers are selected from one or more of the following classifiers: SVM Classifier, Random Forest Classifier, Decision Tree Classifier, KNN Classifier and Naive Bayes Classifier. 11. A system for classifying input data arrived one by one in time, comprising: a trainer respectively training a group of classifiers with a predetermined number of previous input data whose real classes are obtained as learning samples, wherein the number of the previous input data are increased progressively in a reverse chronological order; a selecter selecting a classifier having a highest accuracy on the previous input data from the group of classifiers based on previous classifying results of the group of classifiers; and a classifier classifying current input data using a classifier selected. 12. The system according to claim 11, the selecter calculates a weight of each classifier in the group of classifiers based on the predetermined number of previous input data whose real classes are obtained, wherein, while a classifier gives a right class, the input data is more previous in time, a contribution thereof to the weight of the classifier is larger than for input data less recent in time; and the selecter selects the classifier whose weight is a highest as the classifier having a highest accuracy on the previous input data. 13. The system according to claim 12, wherein the selecter calculates the weight Wi of each classifier in the group of classifiers is calculated by the following equation: W i = ∑ k = 1 M   1 k  p  ( r k , l k ) wherein N1 represents the number of the predetermined number of the previous input data whose real classes are obtained; wherein k represents a kth previous input data in the previous input data whose real classes are obtained, k=1, M; wherein rk represents a classifying result of an ith classifier on the kth previous input data, and lk represents a real class of the kth previous input data; and wherein when the classifying result of the ith classifier on the kth previous input data is right, p(rk,lk)=1, otherwise, p(rk,lk)=0. 14. The system according to claim 11, wherein the number Si of the learning samples for training each classifier in the group of classifiers with the predetermined number is calculated by: Si=i*N wherein i=1, . . . C, C represents the number of the classifiers in the group of classifiers, and N represents the number of the previous input data for training a first classifier in the group of classifiers. 15. The system according to claim 14, wherein a largest number Q of the previous input data stored by the storage is calculated by: Q=C*N. 16. The system according to claim 11, wherein the group of classifiers are trained using the trainer after accumulating the predetermined number of previous input data whose real classes are obtained. 17. The method according to claim 1, wherein the method eliminates concept drift. 18. A method of data mining, comprising classifying current input data according to claim 1 and data mining using the current input data classified to eliminate concept drift. 19. A non-transitory computer readable storage medium storing codes which can be executed on a information processing equipment to implement a method according to claim 1. 20. A system for classifying input data arriving one by one in time, comprising: a memory storing codes; and a processor, the processor can execute the codes to: respectively train a group of classifiers with a predetermined number of previous input data whose real classes are obtained as learning samples, wherein the number of the previous input data are increased progressively in a reverse chronological order; select a classifier having a highest accuracy on the previous input data from the group of classifiers based on previous classifying results of the group of classifiers; and classify current input data using a classifier selected.