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Principal Model Analysis Based on Bagging PLS and PCA and Its Application in financial statement fraud

Journal of Systems Science and Information 2024年第2期12卷 212-228页

作者： Xiao LIANG Qiwei XIE Chunyan LUO Liang TANG Yi SUN School of Economics and Management Beijing University of TechnologyBeijing 100124China Shanghai Tongtu Semiconductor Technology Co. LtdShanghai 210203China School of Finance Anhui University of Finance&EconomicsBengbu 233000China

Motivated by the Bagging Partial Least Squares(Bagging PLS)and Principal Component Analysis(PCA)algorithms,a novel approach known as Principal Model Analysis(PMA)method is introduced in this *** the proposed PMA algorithm,the PCA and the Bagging PLS are *** this method,multiple PLS models are trained on sub-training sets,derived from the training set using the random sampling with replacement *** regression coefficients of all the sub-PLS models are fused in a joint regression coefficient *** final projection direction is then estimated by performing the PCA on the joint regression coefficient ***,the proposed PMA method is compared with other traditional dimension reduction methods,such as PLS,Bagging PLS,Linear discriminant analysis(LDA)and *** results on six public datasets demonstrate that our proposed method consistently outperforms other approaches in terms of classification performance and exhibits greater ***,it is employed in the application of financial statement fraud *** and other five algorithms are utilized to financial statement fraud which concerned by the academic community,and the results indicate that the classification of PMA surpassed that of the other methods.

关键词： principal model analysis partial least squares principal component analysis dimension reduction ensemble learning financial statement fraud detection

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