Learning from positive examples when the negative class is undetermined- microRNA gene identification

Background: The application of machine learning to classification problems that depend only on positive examples is gaining attention in the computational biology community. We and others have described the use of two-class machine learning to identify novel miRNAs. These methods require the generation of an artificial negative class. However, designation of the negative class can be problematic and if it is not properly done can affect the performance of the classifier dramatically and/or yield a biased estimate of performance. We present a study using one-class machine learning for microRNA (miRNA) discovery and compare one-class to two-class approaches using na

• Type: Journal
• Year: 2008
• Where: ALMOB
• Authors: Malik Yousef, Segun Jung, Louise C. Showe, Michael K. Showe
• Keywords: ALMOB 2008, Machine, Negative Class, Support Vector Machines