[1]PU Lingjie,ZENG Fanhui,WANG Peizhuang.Base point classification algorithm based on factor space theory[J].CAAI Transactions on Intelligent Systems,2020,15(3):528-536.[doi:10.11992/tis.201903031]
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Base point classification algorithm based on factor space theory

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 3 Page number: 528-536 Column: 学术论文—人工智能基础 Public date: 2020-05-05
Title:
Base point classification algorithm based on factor space theory
Author(s):
PU Lingjie1; 2;  ZENG Fanhui1; 2;  WANG Peizhuang1; 2
1. College of Science, Liaoning Technical University, Fuxin 123000, China;
2. College of Intelligent Engineering and Mathematics, Liaoning Technical University, Fuxin 123000, China
Keywords:
factor space; background base; background base extraction; λ-background base; base point classification algorithm; identify new classes; data classification; background distribution; background relationship
CLC:
TP18
DOI:
10.11992/tis.201903031
Abstract:
At present, the background-based extraction algorithm based on factor space theory has not achieved good results when used in applications. Reasons for its inefficiency are the calculation process is complicated, initialization depends on the extreme values of each factor, and redundancy of the number of base points extracted. Therefore, combining the inner point judgment method and a novel idea, an improved background-based extraction algorithm with simple calculation, independent initialization, and a few number of base points is proposed. Using the improved background-based extraction algorithm, a new data classification algorithm, i.e., base point classification algorithm, is constructed. The algorithm extracts the background base of each type of sample as the prediction model and optimizes the prediction model through the newly defined λ-background base. Finally, numerical experiments show that the base point classification algorithm is simple in principle, easy in construction, strong in generalizing the ability of classification model, and high in accuracy of prediction ability. Moreover, strict-utility model can provide new methods for identifying new classes.
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