A classification rule reduction algorithm based on significance domains

M. Almiñana, L. Escudero, A. Pérez-Martín, A. Rabasa () and L. Santamaría

TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, 2014, vol. 22, issue 1, 397-418

Abstract: Many rule systems generated from decision trees (like CART, ID3, C4.5, etc.) or from direct counting frequency methods (like Apriori) are usually non-significant or even contradictory. Nevertheless, most papers on this subject demonstrate that important reductions can be made to generate rule sets by searching and removing redundancies and conflicts and simplifying the similarities between them. The objective of this paper is to present an algorithm (RBS: Reduction Based on Significance) for allocating a significance value to each rule in the system so that experts may select the rules that should be considered as preferable and understand the exact degree of correlation between the different rule attributes. Significance is calculated from the antecedent frequency and rule frequency parameters for each rule; if the first one is above the minimal level and rule frequency is in a critical interval, its significance ratio is computed by the algorithm. These critical boundaries are calculated by an incremental method and the rule space is divided according to them. The significance function is defined for these intervals. As with other methods of rule reduction, our approach can also be applied to rule sets generated from decision trees or frequency counting algorithms, in an independent way and after the rule set has been created. Three simulated data sets are used to carry out a computational experiment. Other standard data sets from UCI repository (UCI Machine Learning Repository) and two particular data sets with expert interpretation are used too, in order to obtain a greater consistency. The proposed method offers a more reduced and more easily understandable rule set than the original sets, and highlights the most significant attribute correlations quantifying their influence on consequent attribute. Copyright Sociedad de Estadística e Investigación Operativa 2014

Keywords: Classification rules; Reduction; Significance measures; 62-07; 68U35; 68W40; 68Q25 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1007/s11750-012-0264-6 (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:spr:topjnl:v:22:y:2014:i:1:p:397-418

Ordering information: This journal article can be ordered from
http://link.springer.de/orders.htm

DOI: 10.1007/s11750-012-0264-6

Access Statistics for this article

TOP: An Official Journal of the Spanish Society of Statistics and Operations Research is currently edited by Juan José Salazar González and Gustavo Bergantiños

More articles in TOP: An Official Journal of the Spanish Society of Statistics and Operations Research from Springer, Sociedad de Estadística e Investigación Operativa
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().