A user behavior prediction model based on parallel neural network and k-nearest neighbor algorithms

被引：14
|
作者：
Xu, Gaowei [1 ]
Shen, Carl [2 ]
Liu, Min [1 ]
Zhang, Feng [1 ]
Shen, Weiming [3 ]
机构：
[1] Tongji Univ, Sch Elect & Informat Engn, Shanghai, Peoples R China
[2] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON, Canada
[3] Tongji Univ, Key Lab Embedded Syst & Serv Comp, Shanghai, Peoples R China
来源：
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2017年 / 20卷 / 02期
基金：
美国国家科学基金会;
关键词：
Smart home; Back propagation neural network; k-nearest neighbor; User behavior prediction;
D O I：
10.1007/s10586-017-0749-z
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In the last decade, we have witnessed the dramatic development of the smart home industry. Smart home systems are currently facing an explosive growth of data. Making good use of this vast amount of data has become an attractive research topic in recent years. In order to develop smart home systems' abilities for learning users' behaviors autonomously and offering services spontaneously, a user behavior prediction model based on parallel back propagation neural network (BPNN) and k-nearest neighbor (KNN) algorithms is introduced in this paper. Based on MapReduce, a parallel BPNN algorithm is proposed to improve the prediction accuracy and speed, and a parallel KNN algorithm is developed for user decision-making rule selection. The experimental results indicate that the proposed model is significantly better than traditional user behavior prediction models in term of prediction accuracy and speed. A case study on smart home also illustrates the effectiveness of the proposed model.
引用
收藏
页码：1703 / 1715
页数：13
相关论文
共 50 条
  • [1] A user behavior prediction model based on parallel neural network and k-nearest neighbor algorithms
    Gaowei Xu
    Carl Shen
    Min Liu
    Feng Zhang
    Weiming Shen
    Cluster Computing, 2017, 20 : 1703 - 1715
  • [2] An RBF Neural Network Clustering Algorithm Based on K-Nearest Neighbor
    Li, Jitao
    Xu, Chugui
    Liang, Yongquan
    Wu, Gengkun
    Liang, Zhao
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
  • [3] A Neural Network Based Distance Function for the k-Nearest Neighbor Classifier
    Vajda, Szilard
    Szocs, Barna
    2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2014, : 429 - 433
  • [4] Point Cloud Model Recognition and Classification Based on K-Nearest Neighbor Convolutional Neural Network
    Yu Ting
    Yang Jun
    LASER & OPTOELECTRONICS PROGRESS, 2020, 57 (10)
  • [5] Twin neural network improved k-nearest neighbor regression
    Wetzel, Sebastian J.
    INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, 2024,
  • [6] Prediction of heart conditions by consensus K-nearest neighbor algorithm and convolution neural network
    Waris, Saiyed Faiayaz
    Koteeswaran, S.
    INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING, 2022, 13 (04)
  • [7] A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network
    Salari, Nader
    Shohaimi, Shamarina
    Najafi, Farid
    Nallappan, Meenakshii
    Karishnarajah, Isthrinayagy
    PLOS ONE, 2014, 9 (11):
  • [8] Indoor Localization Using K-nearest Neighbor and Artificial Neural Network Back Propagation Algorithms
    Adege, Abebe Belay
    Yayeh, Yirga
    Berie, Getaneh
    Lin, Hsin-piao
    Yen, Lei
    Li, Yun Ruei
    2018 27TH WIRELESS AND OPTICAL COMMUNICATION CONFERENCE (WOCC), 2018, : 184 - 185
  • [9] Spatial Data Prediction Model Integrated with K-Nearest Neighbor Mechanism in Neural Networks
    Song, Xin
    Zhu, Liang
    Zhang, Yu
    Liu, Haibo
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2024, 38 (05)
  • [10] On neural-network implementations of k-nearest neighbor pattern classifiers
    Chen, YQ
    Damper, RI
    Nixon, MS
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS, 1997, 44 (07): : 622 - 629
12345