Pairwise feature evaluation for constructing reduced representations

Feature selection methods are often used to determine a small set of informative features that guarantee good classification results. Such procedures usually consist of two components: a separability criterion and a selection strategy. The most basic choices for the latter are individual ranking, forward search and backward search. Many intermediate methods such as floating search are also available. The forward as well as backward selection may cause lossy evaluation of the criterion and/or overtraining of the final classifier in case of high-dimensional spaces and small sample size problems. Backward selection may also become computationally prohibitive. Individual ranking, on the other hand, suffers as it neglects dependencies between features. A new strategy based on a pairwise evaluation has recently been proposed by Bo and Jonassen (Genome Biol 3, 2002) and Pezkalska et al. (International Conference on Computer Recognition Systems, Poland, pp 271–278, 2005). Since it consid...

• Type: Journal
• Year: 2007
• Where: PAA
• Authors: Artsiom Harol, Carmen Lai, Elzbieta Pekalska, Robert P. W. Duin
• Keywords: Distributed And Parallel Computing, PAA 2007, Sample Size Problem, Selection, Small Sample Size