Method for Screening Samples for Building Prediction Model and Computer Program Product Thereof

A computer program product stored on a non-transitory tangible computer readable recording medium, which, when executed, performs a method for screening samples for building a prediction model, the method comprising:

• obtaining a plurality of sets of first sample data sequentially generated with respect to a target to be predicted, the sets of first sample data comprising:
• a plurality of sets of monitored data;
• and
• a plurality of objective data, wherein the objective data are corresponding to the sets of monitored data in a one-to-one manner and are cause-and-result related;
• performing a clustering step with respect to all of the sets of first sample data for grouping the sets of first sample data with high similarities as one group, thereby forming and obtaining a plurality of first groups;
• searching for at least one of the first groups having the most number of sets of first sample data, thereby obtaining at least one second group;
• determining if the number of the at least one second group is greater than or equal to 2, thus obtaining a first determination result;
• searching for one of the at least one second group having the oldest set of first sample data when the first determination result is yes, thereby obtaining a third group;
• and determining if the number of sets of first data in the third group is smaller than a predetermined number, thus obtaining a second determination result;
• determining if the number of sets of first data in the second group is smaller than the predetermined number when the first determination result is no, thus obtaining a third determination result;
• reserving all of the sets of first sample data for building or refreshing the prediction model when the second determination result or the third determination result is yes, wherein the prediction model is used for predicting a status or behavior of the target;
• discarding the oldest set of first sample data in the third group and reserving the remaining sets of first sample data for building or refreshing the prediction model when the second determination result is no;
• and
• discarding the oldest set of first sample data in the second group and reserving the remaining sets of first sample data for building or refreshing the prediction model when the third determination result is no.