ROC-tree: A Novel Decision Tree Induction Algorithm Based on Receiver Operating Characteristics to Classify Gene Expression Data

Gene expression information from microarray experiments is a primary form of data for biological analysis and can offer insights into disease processes and cellular behaviour. Such datasets are particularly challenging to build classifiers for, due to their very high dimensional nature and small sample size. Decision trees are a seemingly attractive technique for this domain, due to their easily interpretable white box nature and noise resistance. However, existing decision tree methods tend to perform rather poorly for classifying gene expression data. To address this gap, we introduce a new technique for building decision trees that is better suited to this scenario. Our method is based on consideration of the area under the Receiver Operating Characteristics (ROC) curve, to help determine decision tree characteristics, such as node selection and stopping criteria. We experimentally compare our algorithm, called ROCtree, against other well known decision tree techniques, on a number...

• Type: Conference
• Year: 2008
• Where: SDM
• Authors: M. Maruf Hossain, Md. Rafiul Hassan, James Bailey
• Keywords: Data Mining, Decision Tree, Gene Expression, Receiver Operating Characteristics, SDM 2008