Relevant Attribute Discovery in High Dimensional Data Based on Rough Sets Applications to Leukemia Gene Expressions

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ConferenceThe Tenth International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2005) - Lecture Notes in Computer Sciences / Lecture Notes in Artificial Intelligence, August 31 - September 3, 2005., Regina, Saskatchewan, Canada
AbstractA pipelined approach using two clustering algorithms in combination with Rough Sets is investigated for the purpose discovering important combination of attributes in high dimensional data. In many domains, the data objects are described in terms of a large number of features, like in gene expression experiments, or in samples characterized by spectral information. The Leader and several k-means algorithms are used as fast procedures for attribute set simplification of the information systems presented to the rough sets algorithms. The data submatrices described in terms of these features are then discretized w.r.t the decision attribute according to different rough set based schemes. From them, the reducts and their derived rules are extracted, which are applied to test data in order to evaluate the resulting classification accuracy. An exploration of this approach (using Leukemia gene expression data) was conducted in a series of experiments within a high-throughput distributed-computing environment. They led to subsets of genes with high discrimination power. Good results were obtained with no preprocessing applied to the data.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48122
NPARC number8913287
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Record identifiered378d76-e02c-49d9-9c9a-4b18c393e1d6
Record created2009-04-22
Record modified2016-05-09
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