吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (2): 583-588.doi: 10.13229/j.cnki.jdxbgxb201502036

• Orignal Article • Previous Articles     Next Articles

Single batch test algorithm on cost-sensitive uncertain Nave Bayes for uncertain data

ZHANG Xing1, LI Mei1, ZHANG Yang1,NING Ji-feng2   

  1. 1.College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China;
    2.College of Information Engineering, Northwest A&F University, Yangling 712100, China
  • Received:2014-07-22 Online:2015-04-01 Published:2015-04-01

RICH HTML

1   

PDF (PC)

313

Abstract: In this paper, we propose a single batch test algorithm on Cost-sensitive Uncertain Nave Bayes for Uncertain Data (SBT-CSUNB). We define the influence of an uncertain attribute on the total cost in cost-sensitive Nave Bayes Classifier, and put forward a method to fine an optimal batch test strategy. Experiment results on UCI Database demonstrate that the proposed algorithm can effectively reduce the total cost, and the performance is stable with different parameters and under high uncertain rate.

Key words: artificial intelligence, uncertain single batch, uncertain data, cost sensitive

CLC Number: 

  • TP391
[1] Qin B, Xia Y, Prabhakar S, et al. A rule-based classification algorithm for uncertain data[C]∥25th International Conference on Data Engineering.Shanghai: IEEE, 2009: 1633-1640.
[2] Qi B, Xia Y, Li F. DTU: a Decision Tree for Uncertain Data[M]. Berlin Heidelberg:Springer, 2009: 4-15.
[3] Tsang S, Ben K,Yip K Y,et al. Decision trees for uncertain data[J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(1): 64-78.
[4] Bi J, Zhang T. Support vector classification with input data uncertainty[J]. Advances in Neural Information Processing Systems, 2004,17: 161-169.
[5] Yang J, Gunn S. Exploiting uncertain data in support vector classification[C]∥Knowledge Based Intelligent Information and Engineering Systems. Heidelberg.Berlin: Springer, 2007: 148-155.

[6] Yang J, Gunn S. Iterative constraints in support vector classification with uncertain information[J]. Constraint-based Mining and Learning, 2007,1:49-60.
[7] Qin B, Xia Y, Li F. A Bayesian classifier for uncertain data[C]∥Proceedings of the 2010 ACM Symposium on Applied Computing. New York:ACM, 2010: 1010-1014.
[8] Ren J, Lee S D, Chen X, et al. Naive Bayes classification of uncertain data[C]∥Ninth IEEE International Conference on Data Mining.Miami,FL:IEEE, 2009: 944-949.
[9] Ling C X, Yang Q, Wang J, et al. Decision trees with minimal costs[C]∥Proceedings of the Twenty-first International Conference on Machine Learning. New York:ACM, 2004: 69.
[10] Zubek V B, Dietterich T G. Pruning improves heuristic search for cost-sensitive learning[R]. Corvallis, OR: Oregon State University, Dept of Computer Science, 2004.
[11] Turney P. Cost-sensitive classification: empirical evaluation of a hybrid genetic decision tree induction algorithm[J]. Journal of Artificial Intelligence Research (JAIR), 1995, 2:369-409.
[12] Chai X, Deng L, Yang Q, et al. Test-cost sensitive naive bayes classification[C]∥Fourth IEEE International Conference on Data Mining, IEEE, 2004: 51-58.
[13] Turney P. Types of cost in inductive concept learning[C]∥In Workshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning,Stanford 2000:1-7.
[14] Liu M, Zhang Y, Zhang X, et al. Cost-sensitive Decision Tree for Uncertain Data[M]. Heidelberg, Berlin: Springer, 2011: 243-255.
[15] 刘明建,张阳,王勇.代价敏感不确定决策树的不确定单批测试算法研究[J].工程数学学报,2012,29(4):559-566.
Liu Ming-jian, Zhang Yang,Wang Yong. Uncertain single batch test algorithm on cost-sensitive decision tree for uncertain data[J]. Chinese Journal of Engineering Mathematics, 2012,29(4):559-566.
[1] DONG Sa, LIU Da-you, OUYANG Ruo-chuan, ZHU Yun-gang, LI Li-na. Logistic regression classification in networked data with heterophily based on second-order Markov assumption [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1571-1577.
[2] GU Hai-jun, TIAN Ya-qian, CUI Ying. Intelligent interactive agent for home service [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1578-1585.
[3] WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Measurement of graph similarity based on vertical dimension sequence dynamic time warping method [J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[4] ZHANG Hao, ZHAN Meng-ping, GUO Liu-xiang, LI Zhi, LIU Yuan-ning, ZHANG Chun-he, CHANG Hao-wu, WANG Zhi-qiang. Human exogenous plant miRNA cross-kingdom regulatory modeling based on high-throughout data [J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[5] HUANG Lan, JI Lin-ying, YAO Gang, ZHAI Rui-feng, BAI Tian. Construction of disease-symptom semantic net for misdiagnosis prompt [J]. 吉林大学学报(工学版), 2018, 48(3): 859-865.
[6] LI Xiong-fei, FENG Ting-ting, LUO Shi, ZHANG Xiao-li. Automatic music composition algorithm based on recurrent neural network [J]. 吉林大学学报(工学版), 2018, 48(3): 866-873.
[7] LIU Jie, ZHANG Ping, GAO Wan-fu. Feature selection method based on conditional relevance [J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[8] WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Heuristic algorithm of all common subsequences of multiple sequences for measuring multiple graphs similarity [J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[9] YANG Xin, XIA Si-jun, LIU Dong-xue, FEI Shu-min, HU Yin-ji. Target tracking based on improved accelerated gradient under tracking-learning-detection framework [J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.
[10] LIU Xue-juan, YUAN Jia-bin, XU Juan, DUAN Bo-jia. Quantum k-means algorithm [J]. 吉林大学学报(工学版), 2018, 48(2): 539-544.
[11] QU Hui-yan, ZHAO Wei, QIN Ai-hong. A fast collision detection algorithm based on optimization operator [J]. 吉林大学学报(工学版), 2017, 47(5): 1598-1603.
[12] LI Jia-fei, SUN Xiao-yu. Clustering method for uncertain data based on spectral decomposition [J]. 吉林大学学报(工学版), 2017, 47(5): 1604-1611.
[13] SHAO Ke-yong, CHEN Feng, WANG Ting-ting, WANG Ji-chi, ZHOU Li-peng. Full state based adaptive control of fractional order chaotic system without equilibrium point [J]. 吉林大学学报(工学版), 2017, 47(4): 1225-1230.
[14] WANG Sheng-sheng, WANG Chuang-feng, GU Fang-ming. Spatio-temporal reasoning for OPRA direction relation network [J]. 吉林大学学报(工学版), 2017, 47(4): 1238-1243.
[15] MA Miao, LI Yi-bin. Multi-level image sequences and convolutional neural networks based human action recognition method [J]. 吉林大学学报(工学版), 2017, 47(4): 1244-1252.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd