[1]WU Han,WANG Shitong.Privileged LSSVM for classification and simultaneous importance identification of missing information on incomplete data[J].CAAI Transactions on Intelligent Systems,2023,18(4):743-753.[doi:10.11992/tis.202202026]
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Privileged LSSVM for classification and simultaneous importance identification of missing information on incomplete data

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 4 Page number: 743-753 Column: 学术论文—机器感知与模式识别 Public date: 2023-07-15
Title:
Privileged LSSVM for classification and simultaneous importance identification of missing information on incomplete data
Author(s):
WU Han;  WANG Shitong
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Keywords:
least squares support vector machines; learning using privileged information; additional kernel; missing data; k-nearest neighbor; sample space; privileged space; data quality
CLC:
TP181
DOI:
10.11992/tis.202202026
Abstract:
While handling missing data classification tasks, the commonly-used removal strategy of missing data may perhaps degrade the classifier’s performance, due to very insufficient perfect data. Based on the strategy of processing missing data and constructing classification model simultaneously, we develop a novel privileged LSSVM (P-LSSVM), which learns using privilaged information. It can not only improve its classification performance, but also determines the importance of missing features without bias. The basic idea is to take the trained classifier of the available perfect data as the privileged information to guide the learning of LSSVM for the whole incomplete data, express the importance of each feature including missing features through the additivity kernel, then deduce the privilaged information of complete data after training, based on which P-LSSVM is constructed. Finally, the unbiased missing feature importance recognition is completed by the proposed leaving-one cross-validation method. Experimental results show that the proposed method can achieve better testing accuracies, with the importance identification of missing features.
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