ct 16(7): e3

Research Article

Implementation of Human Cognitive Bias on Naïve Bayes

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  • @ARTICLE{10.4108/eai.3-12-2015.2262494,
    author={Hidetaka Taniguchi and Tomohiro Shirakawa and Tatsuji Takahashi},
    title={Implementation of Human Cognitive Bias on Na\~{n}ve Bayes},
    journal={EAI Endorsed Transactions on Creative Technologies},
    volume={3},
    number={7},
    publisher={ACM},
    journal_a={CT},
    year={2016},
    month={5},
    keywords={na\~{n}ve bayes, text classification, attribute independence assumption, cognition-inspired model, bayesian spam filtering},
    doi={10.4108/eai.3-12-2015.2262494}
}
  • Hidetaka Taniguchi
    Tomohiro Shirakawa
    Tatsuji Takahashi
    Year: 2016
    Implementation of Human Cognitive Bias on Naïve Bayes
    CT
    EAI
    DOI: 10.4108/eai.3-12-2015.2262494
Hidetaka Taniguchi1,*, Tomohiro Shirakawa2, Tatsuji Takahashi1
  • 1: Tokyo Denki University, School of Science and Engineering
  • 2: National Defense Academy of Japan, Department of Computer Science
*Contact email: htdendai@gmail.com

Abstract

We propose a human-cognition inspired classification model based on Naïve Bayes. Our previous study showed that human-cognitively inspired heuristics is able to enhance the prediction accuracy of text classifier based on Naïve Bayes. In the study, our classification model showed higher performance than conventional Naïve Bayes under specific conditions. In this paper, to investigate the mechanism that realizes the higher performance of classification, we further tested our model and its modified variant. As a result, our two models showed slightly different behaviors, but both of them achieved higher performance than conventional Naïve Bayes.

Keywords
naïve bayes, text classification, attribute independence assumption, cognition-inspired model, bayesian spam filtering
Published
2016-05-24
Publisher
ACM
http://dx.doi.org/10.4108/eai.3-12-2015.2262494

Copyright © 2015 H.Taniguchi et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.