Experience of Using SVM for the Triage Task in TREC 2004 Genomics Track

This paper reports our knowledge-ignorant machine learning approach to the triage task in TREC2004 genomics track, which is actually a text categorization problem. We applied Support Vector Machine (SVM) and found that information-gain based feature selection is helpful. Although we achieved decent performance in leave-one-out cross-validation experiments, the evaluation result on the test data turned out to be surprisingly poor. Further experiments revealed that there is a chasm between the training and test data distributions. It seems that more aggressive feature selection can partially alleviate the trouble caused by distribution change. Keywords Text Categorization, Machine Learning, Support Vector Machine, Feature Selection, Distribution Change.

• Type: Conference
• Year: 2004
• Where: TREC
• Authors: Dell Zhang, Wee Sun Lee
• Keywords: Feature Selection, Machine Learning, Support Vector Machine, TREC 2004, TREC 2008