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Research Article

A computing method of predictive value based on fitting function in linear model

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  • @ARTICLE{10.4108/eai.2-10-2020.166542,
        author={Hao Zhong and Huibing Zhang and Fei Jia},
        title={A computing method of predictive value based on fitting function in linear model},
        journal={EAI Endorsed Transactions on Collaborative Computing: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={CC},
        year={2020},
        month={10},
        keywords={linear model, linear fitting, fitting function, predictive value},
        doi={10.4108/eai.2-10-2020.166542}
    }
    
  • Hao Zhong
    Huibing Zhang
    Fei Jia
    Year: 2020
    A computing method of predictive value based on fitting function in linear model
    CC
    EAI
    DOI: 10.4108/eai.2-10-2020.166542
Hao Zhong1,*, Huibing Zhang2, Fei Jia2
  • 1: School of Computer Science, South China Normal University, Guangzhou 510631, China
  • 2: Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
*Contact email: scnuzhonghao@foxmail.com

Abstract

Linear models are common prediction models in collaborative computing, which mainly generates fitting function to express the relationship between feature vectors and predictive value. In the process of computing the predictive value according to the fitting function and feature vector, this paper mainly conducted the following researches. Firstly, this paper defines a change interval of predictive value according to training set. Secondly, in this paper, the change interval of predictive value corresponding to feature vector in test setis computed. Finally, according to distribution of training set in the changing interval, the predictive values corresponding to feature vectors in test set are computed. Standard data sets are used in experiment, and MAE(Mean Absolute Error) and RMSE(Root Mean Square Error) are used to evaluate the prediction results. The experimental results show that the method proposed in this paper can improve the prediction error to acertain extent.