PROBLEM TO BE SOLVED: To provide a method for screening samples for building a prediction model and a computer program product thereof. SOLUTION: When a set of new sample data is added to a dynamic moving window, a clustering step is performed with respect to all of the sets of sample data within the window for grouping the sets of sample data with similar properties as one group. Then, the number of sets of sample data in each group is inspected. If the number of the sets of sample data in the largest group is greater than a predetermined threshold, it means that there are too many sets of sample data with similar properties in the largest group, and the oldest sample data in the largest group can be deleted. If the number of the sets of sample data in the largest group is smaller than or equal to a predetermined threshold, it means that the sample data in the largest group are quite unique, and should be kept for building or refreshing the prediction model.
Original language  Chinese 

Patent number  特許第5515125號 

Publication status  Published  1800 

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@misc{4eaaa26dfa4d47cf952200f49e1b2b66,
title = "預測模型之建模樣本的篩選方法及其電腦程式產品",
abstract = "PROBLEM TO BE SOLVED: To provide a method for screening samples for building a prediction model and a computer program product thereof. SOLUTION: When a set of new sample data is added to a dynamic moving window, a clustering step is performed with respect to all of the sets of sample data within the window for grouping the sets of sample data with similar properties as one group. Then, the number of sets of sample data in each group is inspected. If the number of the sets of sample data in the largest group is greater than a predetermined threshold, it means that there are too many sets of sample data with similar properties in the largest group, and the oldest sample data in the largest group can be deleted. If the number of the sets of sample data in the largest group is smaller than or equal to a predetermined threshold, it means that the sample data in the largest group are quite unique, and should be kept for building or refreshing the prediction model.",
author = "FanTien Cheng",
year = "1800",
language = "Chinese",
type = "Patent",
note = "特許第5515125號",
}
TY  PAT
T1  預測模型之建模樣本的篩選方法及其電腦程式產品
AU  Cheng, FanTien
PY  1800
Y1  1800
N2  PROBLEM TO BE SOLVED: To provide a method for screening samples for building a prediction model and a computer program product thereof. SOLUTION: When a set of new sample data is added to a dynamic moving window, a clustering step is performed with respect to all of the sets of sample data within the window for grouping the sets of sample data with similar properties as one group. Then, the number of sets of sample data in each group is inspected. If the number of the sets of sample data in the largest group is greater than a predetermined threshold, it means that there are too many sets of sample data with similar properties in the largest group, and the oldest sample data in the largest group can be deleted. If the number of the sets of sample data in the largest group is smaller than or equal to a predetermined threshold, it means that the sample data in the largest group are quite unique, and should be kept for building or refreshing the prediction model.
AB  PROBLEM TO BE SOLVED: To provide a method for screening samples for building a prediction model and a computer program product thereof. SOLUTION: When a set of new sample data is added to a dynamic moving window, a clustering step is performed with respect to all of the sets of sample data within the window for grouping the sets of sample data with similar properties as one group. Then, the number of sets of sample data in each group is inspected. If the number of the sets of sample data in the largest group is greater than a predetermined threshold, it means that there are too many sets of sample data with similar properties in the largest group, and the oldest sample data in the largest group can be deleted. If the number of the sets of sample data in the largest group is smaller than or equal to a predetermined threshold, it means that the sample data in the largest group are quite unique, and should be kept for building or refreshing the prediction model.
M3  Patent
M1  特許第5515125號
ER 