A data analysis system includes a modeling unit, a feature-extraction unit, a processing unit and an output unit. The modeling unit creates a prediction model by a machine learning algorithm according to training data. The feature-extraction unit extracts a plurality of fragment of feature data of input data, and classifies the feature data into a plurality of groups. The processing unit obtains a probability of the input data corresponding to the prediction model by the machine learning algorithm according to the feature of one of the groups, and determines the probability. When the probability is less than a predetermined value, the processing unit uses another feature data corresponding to another group which is not used to renew the probability of the input data corresponding to the prediction model through the machine learning algorithm. When the probability is greater than or equal to the predetermined value, the processing unit classifies the input data. The output unit outputs a classification result.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data analysis system, comprising: a modeling unit, using a machine learning algorithm to create a prediction model by training data; a feature-extraction unit, extracting a plurality of fragments of feature data of input data, and classifying the feature data into a plurality of groups; a processing unit, obtaining a probability of the input data corresponding to the prediction model by the machine learning algorithm according to the feature data corresponding to one of the groups, and determining the probability, wherein the processing unit selects the feature data of one of the unselected groups when the probability is less than a predetermined value; updating the probability of the input data corresponding to the prediction model by the machine learning algorithm; and classifying the input data according to the probability when the probability is greater than or equal to the predetermined value; and an output unit, outputting a classification result. 2. The data analysis system as claimed in claim 1, wherein the processing unit determines that a classification of the input data is different from the training data when there is no unselected group and the probability is less than the predetermined value. 3. The data analysis system as claimed in claim 1, wherein the classification of the input data is the same as the training data when the probability is greater than or equal to the predetermined value. 4. The data analysis system as claimed in claim 1, wherein the feature-extraction unit further classifies the groups according to a predetermined weighting of the input data corresponding to the machine learning algorithm. 5. A data analysis method, comprising: creating a prediction model by a machine learning algorithm according to training data; receiving input data comprising a plurality of fragment of feature data; extracting the feature data of the input data, and classifying the feature data into a plurality of groups; obtaining a probability of the feature data by the machine learning algorithm according to the feature data of one of the groups; and determining the probability, wherein the feature data of one of the unselected groups is selected when the probability is less than a predetermined value, the probability of the input data corresponding to the prediction model will be updated by the machine learning algorithm according to the selected feature data, and the input data will be classified when the probability greater than or equal to the predetermined value. 6. The data analysis method as claimed in claim 5, further comprising: determining that a classification of the input data is different from the training data when there is no unselected-group and the probability is less than the predetermined value. 7. The data analysis method as claimed in claim 5, further comprising: determining that the classification of the input data is the same as the training data when the probability is greater than or equal to the predetermined value. 8. A data analysis system, comprising: a modeling unit, creating a prediction model by a machine learning algorithm according to training data; a feature-extraction unit, extracting a plurality of piece of feature data of input data, and classifying the feature data into a first group and a second group a processing unit, obtaining a first probability and a second probability corresponding to the prediction model according to a part of feature data corresponding to the first group and a part of feature data corresponding to the second group respectively, and obtaining a determination result by selecting all feature data of the first group or the second group according to the first probability and the second probability; and an output unit, outputting the determination result. 9. The data analysis system as claimed in claim 8, wherein the processing unit updates the first probability by the machine learning algorithm according to all of the feature data corresponding to the first group when the first probability is greater than or equal to the second probability, determines whether the first probability is greater than or equal to a predetermined value, and obtains the determination result according to the first probability when the first probability is greater than or equal to the predetermined value. 10. The data analysis system as claimed in claim 8, wherein the processing unit updates the second probability by the machine learning algorithm according to all of the feature data corresponding to the second group when the first probability is less than the second probability, and obtains the determination result according to the second probability. 11. The data analysis system as claimed in claim 8, wherein the feature-extraction unit further classifies the feature data into the first group and the second group according to a time function. 12. A data analysis method, comprising: creating a prediction model by a machine learning algorithm according to training data; receiving input data comprising a plurality of fragment of feature data; classifying the feature data into a first group and a second group; obtaining a first probability and a second probability corresponding to the prediction model according to a part of feature data corresponding to the first group and a part of feature data corresponding to the second group respectively; obtaining a determination result by selecting all feature data of the first group or the second group according to the first probability and the second probability; and outputting the determination result. 13. The data analysis method as claimed in claim 12, further comprising: updating the first probability by the machine learning algorithm according to all of the feature data corresponding to the first group when the first probability is greater than or equal to the second probability, determining whether the first probability is greater than or equal to a predetermined value; and obtaining the determination result according to the first probability when the first probability is greater than or equal to the predetermined value. 14. The data analysis method as claimed in claim 12, further comprising: updating the second probability by the machine learning algorithm according to all of the feature data corresponding to the second group when the first probability is less than the second probability; and obtaining the determination result according to the second probability.