CD: A Coupled Discretization Algorithm

Publisher:
Springer
Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science, 2012, 7302 pp. 407 - 418
Issue Date:
2012-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2012001104OK.pdfPublished Version354.26 kB
Adobe PDF
Discretization technique plays an important role in data mining and machine learning. While numeric data is predominant in the real world, many algorithms in supervised learning are restricted to discrete variables. Thus, a variety of research has been conducted on discretization, which is a process of converting the continuous attribute values into limited intervals. Recent work derived from entropy-based discretization methods, which has produced impressive results, introduces information attribute dependency to reduce the uncertainty level of a decision table; but no attention is given to the increment of certainty degree from the aspect of positive domain ratio. This paper proposes a discretization algorithm based on both positive domain and its coupling with information entropy, which not only considers information attribute dependency but also concerns deterministic feature relationship. Substantial experiments on extensive UCI data sets provide evidence that our proposed coupled discretization algorithm generally outperforms other seven existing methods and the positive domain based algorithm proposed in this paper, in terms of simplicity, stability, consistency, and accuracy.
Please use this identifier to cite or link to this item: