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Landscapes of Naïve Bayes classifiers

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Abstract

The performance of the Naïve Bayes classifier (NB) is of interest to many researchers. The desire to improve upon the apparent good performance of NB while maintaining its efficiency and simplicity is demonstrated by the variety of adaptations to NB in the literature. This study takes a look at 37 such adaptations. The idea is to give a qualitative overview of the adaptations rather than a quantitative analysis of their performance. Landscapes are produced using Sammon mapping, Principal Component Analysis (PCA) and Self-Organising feature Maps (SOM). Based on these, the methods are split into five main groups—tree structures, feature selection, space transformation, Bayesian networks and joint features. The landscapes can also be used for placing any new variant of NB to obtain its nearest neighbours as an aid for comparison studies.

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Notes

  1. Developed at The Laboratory of Computer and Information Science (CIS), Department of Computer Science and Engineering at the Helsinki University of Technology. http://www.cis.hut.fi/projects/somtoolbox/

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  1. School of Informatics, University of Wales Bangor, Bangor, UK

    Zoë Hoare

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  1. Zoë Hoare
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Correspondence to Zoë Hoare.

Data matrix

Data matrix

Table 4 Data matrix encoding each of the 38 methods using the 19 selected characteristics
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Hoare, Z. Landscapes of Naïve Bayes classifiers. Pattern Anal Applic 11, 59–72 (2008). https://doi.org/10.1007/s10044-007-0079-5

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