In this paper, we propose a robust algorithm to find out rule groups that describe a specific class in high dimensional gene expression datasets.
Finding Rule Groups to Classify High Dimensional Gene Expression ...
dl.acm.org › doi › ICPR.2006.564
This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. ... Our experiments show that the rule groups obtained by our ...
Microarray data provides quantitative information about the transcription profile of cells. To ana- lyze microarray datasets, methodology of machine ...
Finding Rule Groups to Classify High Dimensional Gene ... - dblp
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Bibliographic details on Finding Rule Groups to Classify High Dimensional Gene Expression Datasets.
ABSTRACT. In this paper, we propose a novel algorithm to discover the top- k covering rule groups for each row of gene expression profiles.
A popular type of exploratory tool for finding subgroups is cluster analysis, and many different flavors of algorithms have been used and indeed tailored for ...
In this chapter, we present several state-of-art techniques for analyzing high- dimensional data, e.g., frequent pattern mining, clustering, and classification.
Oct 22, 2024 · Finding Rule Groups to Classify High Dimensional Gene Expression Datasets. Article. Sep 2008. Jiyuan An · Yi-Ping ...
Feb 13, 2024 · The approach first applies the minimum redundancy maximum relevancy (MRMR) filter method to reduce feature dimensionality and then uses the ...
Sep 10, 2024 · We propose a knowledge-slanted RF that integrates biological networks as prior knowledge into the model to improve its performance and explainability.