Output details
11 - Computer Science and Informatics
University of Nottingham
Article title
A 'non-parametric' version of the naive Bayes classifier
Type
D - Journal article
Title of journal
Knowledge-Based Systems
Article number
-
Volume number
24
Issue number
6
First page of article
775
ISSN of journal
0950-7051
Year of publication
2011
Number of additional authors
4
Additional information
<24> This paper introduces a new algorithm for classification, taking into account important features of the data under consideration. Based on the well-known naïve Bayes classifier, the novel methodology presented in this publication is applicable to a wider range of real-world data, since its underlying assumptions are not as strict as the original algorithm. By carrying out different tests on diverse data sets, we demonstrate that this new methodology offers a competitive alternative to the naïve Bayes classifier which can be used to tackle many real-world classification problems. Knowledge-Based Systems has an impact factor of 4.104.
Interdisciplinary
Yes
Cross-referral requested
-
Research group
None
Citation count
8
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
