Scalable Rule-Based Gene Expression Data Classification

Abstract-- Current state-of-the-art association rule-based classifiers for gene expression data operate in two phases: (i) Association rule mining from training data followed by (ii) Classification of query data using the mined rules. In the worst case, these methods require an exponential search over the subset space of the training data set's samples and/or genes during at least one of these two phases. Hence, existing association rulebased techniques are prohibitively computationally expensive on large gene expression datasets. Our main result is the development of a heuristic rule-based gene expression data classifier called Boolean Structure Table Classification (BSTC). BSTC is explicitly related to association rule-based methods, but is guaranteed to be polynomial space/time. Extensive cross validation studies on several real gene expression datasets demonstrate that BSTC retains the classification accuracy of current association rule-based methods while being orders of magn...

• Type: Conference
• Year: 2008
• Where: ICDE
• Authors: Mark A. Iwen, Willis Lang, Jignesh M. Patel
• Keywords: Association Rule-based Methods, Database, Gene Expression Datasets, ICDE 2008, Rule-based Gene Expression