Computer Science ›› 2017, Vol. 44 ›› Issue (6): 212-215.doi: 10.11896/j.issn.1002-137X.2017.06.035

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Classification Algorithm Using Linear Regression and Attribute Ensemble

QIANG Bao-hua, TANG Bo, WANG Yu-feng, ZOU Xian-chun, LIU Zheng-li, SUN Zhong-xu and XIE Wu   

  • Online:2018-11-13 Published:2018-11-13

Abstract: For the classification problems of high-dimensionality and small-sample data,the predictive accuracy of the classification model is restricted by the complexity of the high dimensional attributes.To further improve the accuracy,a classification algorithm using linear regression and attributes ensemble (LRAE) was proposed.The linear regression is utilized to construct an attribute linear classifier (ALC) for each attribute.To avoid the decrease of accuracy caused by too many ALCs,empirical loss value in the empirical risk minimization strategy is used as the evaluation criteria to select ALCs.The majority voting method is adopted to integrate ALCs.The results of experiments using gene expression data demonstrate that the accuracy of LRAE algorithm is relatively higher than that of logistic regression,support vector machine and random forest algorithms.

Key words: Linear regression,Single attribute classification,Empirical loss,Attribute ensemble,Majority voting method

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