Imprecise information in Bayes classifier

被引：0
|
作者：
Robert Burduk
机构：
[1] Wroclaw University of Technology,Department of Systems and Computer Networks
来源：
Pattern Analysis and Applications | 2012年 / 15卷
关键词：
Bayes decision rule; Imprecise observations; Probability of error;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The paper considers the problem of classification error in pattern recognition. This model of classification is primarily based on the Bayes rule and secondarily on the notion of intuitionistic or interval-valued fuzzy sets. A probability of misclassifications is derived for a classifier under the assumption that the features are class-conditionally statistically independent, and we have intuitionistic or interval-valued fuzzy information on object features instead of exact information. A probability of the intuitionistic or interval-valued fuzzy event is represented by the real number. Additionally, the received results are compared with the bound on the probability of error based on information energy. Numerical example concludes the work.
引用
收藏
页码：147 / 153
页数：6
相关论文
共 50 条
  • [1] Imprecise information in Bayes classifier
    Burduk, Robert
    [J]. PATTERN ANALYSIS AND APPLICATIONS, 2012, 15 (02) : 147 - 153
  • [2] BAYES SHARPENING OF IMPRECISE INFORMATION
    Kulczycki, Piotr
    Charytanowicz, Malgorzata
    [J]. INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE, 2005, 15 (03) : 393 - 404
  • [3] Dynamic Classifier Selection Based on Imprecise Probabilities: A Case Study for the Naive Bayes Classifier
    Li, Meizhu
    De Bock, Jasper
    de Cooman, Gert
    [J]. UNCERTAINTY MODELLING IN DATA SCIENCE, 2019, 832 : 149 - 156
  • [4] On the Use of m-Probability-Estimation and Imprecise Probabilities in the Naive Bayes Classifier
    Castellano, Javier G.
    Moral-Garcia, Serafin
    Mantas, Carlos J.
    Abellan, Joaquin
    [J]. INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2020, 28 (04) : 661 - 682
  • [5] Making up the shortages of the Bayes classifier by the maximum mutual information classifier
    Lu, Chenguang
    Zou, Xiaohui
    Wang, Wenfeng
    Chen, Xiaofeng
    [J]. JOURNAL OF ENGINEERING-JOE, 2020, 2020 (13): : 659 - 663
  • [6] Bayes' Theorem Based Maritime Safety Information Classifier
    Liu, Hongze
    Liu, Zhengjiang
    Wang, Xin
    Cai, Yao
    [J]. PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC), 2018, : 2725 - 2729
  • [7] A Robust Dynamic Classifier Selection Approach for Hyperspectral Images with Imprecise Label Information
    Li, Meizhu
    Huang, Shaoguang
    De Bock, Jasper
    de Cooman, Gert
    Pizurica, Aleksandra
    [J]. SENSORS, 2020, 20 (18) : 1 - 28
  • [8] ON THE MULTISTAGE BAYES CLASSIFIER
    KURZYNSKI, MW
    [J]. PATTERN RECOGNITION, 1988, 21 (04) : 355 - 365
  • [9] Growing a tree classifier with imprecise data
    Ciampi, A
    Diday, E
    Lebbe, J
    Périnel, E
    Vignes, R
    [J]. PATTERN RECOGNITION LETTERS, 2000, 21 (09) : 787 - 803
  • [10] Robust Bayes Factor for Independent Two-Sample Comparisons under Imprecise Prior Information
    Ebner, Luisa
    Schwaferts, Patrick
    Augustin, Thomas
    [J]. PROCEEDINGS OF THE ELEVENTH INTERNATIONAL SYMPOSIUM ON IMPRECISE PROBABILITIES: THEORIES AND APPLICATIONS (ISIPTA 2019), 2019, 103 : 167 - 174
12345