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  • Open Access

    ARTICLE

    A Model Average Algorithm for Housing Price Forecast with Evaluation Interpretation

    Jintao Fu1, Yong Zhou1,*, Qian Qiu2, Guangwei Xu3, Neng Wan3

    Journal of Quantum Computing, Vol.4, No.3, pp. 147-163, 2022, DOI:10.32604/jqc.2022.038358

    Abstract In the field of computer research, the increase of data in result of societal progress has been remarkable, and the management of this data and the analysis of linked businesses have grown in popularity. There are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home prices. Using regression methods in machine learning to segment the data set, examine the major factors affecting it, and forecast home prices is the most popular method for examining pricing information. It is challenging to generate precise forecasts since many of the regression… More >

  • Open Access

    ARTICLE

    Improved Logistic Regression Algorithm Based on Kernel Density Estimation for Multi-Classification with Non-Equilibrium Samples

    Yang Yu1, Zeyu Xiong1,*, Yueshan Xiong1, Weizi Li2

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 103-118, 2019, DOI:10.32604/cmc.2019.05154

    Abstract Logistic regression is often used to solve linear binary classification problems such as machine vision, speech recognition, and handwriting recognition. However, it usually fails to solve certain nonlinear multi-classification problem, such as problem with non-equilibrium samples. Many scholars have proposed some methods, such as neural network, least square support vector machine, AdaBoost meta-algorithm, etc. These methods essentially belong to machine learning categories. In this work, based on the probability theory and statistical principle, we propose an improved logistic regression algorithm based on kernel density estimation for solving nonlinear multi-classification. We have compared our approach with other methods using non-equilibrium samples,… More >

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