Fuzzy clustering-based discretization for gene expression classification |
| |
Authors: | Keivan Kianmehr Mohammed Alshalalfa Reda Alhajj |
| |
Affiliation: | 1. Department of Computer Science, University of Calgary, Calgary, AB, Canada 2. Department of Computer Science, Global University, Beirut, Lebanon
|
| |
Abstract: | This paper presents a novel classification approach that integrates fuzzy class association rules and support vector machines.
A fuzzy discretization technique based on fuzzy c-means clustering algorithm is employed to transform the training set, particularly
quantitative attributes, to a format appropriate for association rule mining. A hill-climbing procedure is adapted for automatic
thresholds adjustment and fuzzy class association rules are mined accordingly. The compatibility between the generated rules
and fuzzy patterns is considered to construct a set of feature vectors, which are used to generate a classifier. The reported
test results show that compatibility rule-based feature vectors present a highly- qualified source of discrimination knowledge
that can substantially impact the prediction power of the final classifier. In order to evaluate the applicability of the
proposed method to a variety of domains, it is also utilized for the popular task of gene expression classification. Further,
we show how this method provide biologists with an accurate and more understandable classifier model compared to other machine
learning techniques. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|