Ensemble stump classifiers and gene expression signatures in lung cancer.
Microarray data sets for cancer tumor tissue generally have very few samples, each sample having thousands of probes (i.e., continuous variables). The sparsity of samples makes it difficult for machine learning techniques to discover probes relevant to the classification of tumor tissue. By combining data from different platforms (i.e., data sources), data sparsity is reduced, but this typically requires normalizing data from the different platforms, which can be non-trivial. This paper proposes a variant on the idea of ensemble learners to circumvent the need for normalization. To facilitate comprehension we build ensembles of very simple classifiers known as decision stumps--decision trees of one test each. The Ensemble Stump Classifier (ESC) identifies an mRNA signature having three probes and high accuracy for distinguishing between adenocarcinoma and squamous cell carcinoma of the lung across four data sets. In terms of accuracy, ESC outperforms a decision tree classifier on all four data sets, outperforms ensemble decision trees on three data sets, and simple stump classifiers on two data sets.
Frey
Lewis
L
Biomedical Informatics Department, University of Utah, Salt Lake City 84112, USA.
Edgerton
Mary
M
Fisher
Douglas
D
Levy
Shawn
S
D001185:Artificial Intelligence; D019295:Computational Biology; D018076:DNA, Complementary; D003663:Decision Trees; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis
D018076:DNA, Complementary