A Rules Reduction Algorithm Based On
Significance Measure
Author(s)
R. Abadía, M. Almiñana, L. F. Escudero, A. Pérez-Martín,
A. Rabasa & L. Santamaría
Abstract
A lot of rules systems generated from decision trees (like CART, ID3, C4.5, etc)
or from counting frequencies direct methods, usually provides non-significant or
even contradictory rules. Most existing papers on the subject reach very
important reductions over generated rules sets by searching and removing
redundancies and conflicts and simplifying similarities between them.
In this paper we propose an algorithm (RBS: Reduction Based on
Significance) for allocating a significance value to each rule in the system. This
significance value may be used by experts to point out which of these rules must
be considered preferable and to understand the exact correlation degree between
different rule attributes. The significance is calculated from support and
confidence parameters. For each rule, if its support is over a minimum level and
its confidence is into a critical interval, its significance ratio is calculated by the
algorithm. Thus, the rules space is divided according to these critical boundaries
which are calculated by an incremental method. Finally, the significance function
is defined in each of these intervals.
Like other rules reduction methods, our approach can also be applied to rules
sets generated from decision trees or frequency counting algorithms, in an
absolutely independent way and after the rules set was created. So, our RBS
algorithm does not change the original accuracy of the rules.
The proposed method has been executed over three different data sets: two of
them belong to UCI (University of California, Irvine) standard repository and the
third is a real irrigation data set provided by the users. The validity of our
reduction approach on the later data set is supervised and contrasted by experts.
The computational experience provided in this paper supplies rules sets more
reduced, ordered and easily understandable than the original ones.
Keywords: classification rules, reduction, significance measures, support,
confidence, regions of significance.
Keywords
classification rules, reduction, significance measures, support,
confidence, regions of significance.
Related Book
Other papers in this volume
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