FISA: Feature-based instance selection for imbalanced text classification

• Main Authors: SUN, Aixin, LIM, Ee Peng, Benatallah, Boualem, Hassan, Mahbub
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2006
• Subjects: Vector support machine; Statistical analysis; Electronic discussion group; Classification; Natural language; Text; Information retrieval; Content analysis; Data analysis; Knowledge discovery; Data mining; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/894; https://ink.library.smu.edu.sg/context/sis_research/article/1893/viewcontent/Sun2006_Chapter_FISAFeature_BasedInstanceSelec.pdf
• Institution: Singapore Management University

Support Vector Machines (SVM) classifiers are widely used in text classification tasks and these tasks often involve imbalanced training. In this paper, we specifically address the cases where negative training documents significantly outnumber the positive ones. A generic algorithm known as FISA (F...