Synthetic data generator for classification rules learning
Liu R.3; Fang B.3; Tang Y.Y.2; Chan P.P.K.1
Source PublicationProceedings - 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016
AbstractA standard data set is useful to empirically evaluate classification rules learning algorithms. However, there is still no standard data set which is common enough for various situations. Data sets from the real world are limited to specific applications. The sizes of attributes, the rules and samples of the real data are fixed. A data generator is proposed here to produce synthetic data set which can be as big as the experiments demand. The size of attributes, rules, and samples of the synthetic data sets can be easily changed to meet the demands of evaluation on different learning algorithms. In the generator, related attributes are created at first. And then, rules are created based on the attributes. Samples are produced following the rules. Three decision tree algorithms are evaluated used synthetic data sets produced by the proposed data generator.
KeywordAutomatic decision support Data mining Decision tree Synthetic data
URLView the original
Fulltext Access
Citation statistics
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.South China University of Technology
2.Universidade de Macau
3.Chongqing University
Recommended Citation
GB/T 7714
Liu R.,Fang B.,Tang Y.Y.,et al. Synthetic data generator for classification rules learning[C],2017:357-361.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu R.]'s Articles
[Fang B.]'s Articles
[Tang Y.Y.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu R.]'s Articles
[Fang B.]'s Articles
[Tang Y.Y.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu R.]'s Articles
[Fang B.]'s Articles
[Tang Y.Y.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.