Using multiple and negative target rules to make classifiers more understandable
Article
Article Title | Using multiple and negative target rules to make classifiers more understandable |
---|---|
ERA Journal ID | 18062 |
Article Category | Article |
Authors | Li, Jiuyong (Author) and Jones, Jason (Author) |
Journal Title | Knowledge-Based Systems |
Journal Citation | 19 (6), pp. 438-444 |
Number of Pages | 7 |
Year | 2006 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0950-7051 |
1872-7409 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.knosys.2006.03.003 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0950705106000700 |
Abstract | [Abstract]: One major goal for data mining is to understand data. Rule based methods are better than other methods in making mining results comprehensible. However, current rule based classifiers make use of a small number of rules and a default prediction to build a concise predictive model. This reduces the explanatory ability of the rule based classifier. In this paper, we propose to use multiple and negative target rules to improve explanatory ability of rule based classifiers. We show experimentally that this understandability is not at the cost of accuracy of rule based classifiers. |
Keywords | classification, association rules, negative and multiple rules |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
460299. Artificial intelligence not elsewhere classified | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Department of Mathematics and Computing |
https://research.usq.edu.au/item/9y0wy/using-multiple-and-negative-target-rules-to-make-classifiers-more-understandable
Download files
1889
total views550
total downloads2
views this month1
downloads this month