A case study on partitioning data for classification

Bikash Kanti Sarkar

International Journal of Information and Decision Sciences, 2016, vol. 8, issue 1, 73-91

Abstract: Designing accurate model for classification problem is a real concern in context of machine learning. The various factors such as inclusion of excellent samples in the training set, the number of samples as well as the proportion of each class type in the set (that would be sufficient for designing model) play important roles in this purpose. In this article, an investigation is introduced to address the question of what proportion of the samples should be devoted to the training set for developing a better classification model. The experimental results on several datasets, using C4.5 classifier, shows that any equidistributed data partitioning in between (20%, 80%) and (30%, 70%) may be considered as the best sample partition to build classification model irrespective to domain, size and class imbalanced.

Keywords: case study; data partitioning; classification; C4.5; modelling; machine learning; samples; training sets; sampling; sample partitions. (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=75788 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijidsc:v:8:y:2016:i:1:p:73-91

Access Statistics for this article

More articles in International Journal of Information and Decision Sciences from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().