|Appears in Collections:||Computing Science and Mathematics Book Chapters and Sections|
|Peer Review Status:||Refereed|
|Title:||A Targeted Estimation of Distribution Algorithm Compared to Traditional Methods in Feature Selection|
|Citation:||Neumann G & Cairns D (2016) A Targeted Estimation of Distribution Algorithm Compared to Traditional Methods in Feature Selection. In: Madani K, Dourado A, Rosa A, Filipe J & Kacprzyk J (eds.) Computational Intelligence: Revised and Selected Papers of the International Joint Conference, IJCCI 2013, Vilamoura, Portugal, September 20-22, 2013. Studies in Computational Intelligence, 613. 5th International Joint Conference on Computational Intellegience, IJCCI 2013, Vilamoura, Portugal, 20.09.2013-22.09.2013. Cham, Switzerland: Springer, pp. 83-103. http://link.springer.com/chapter/10.1007/978-3-319-23392-5_5; https://doi.org/10.1007/978-3-319-23392-5_5|
|Keywords:||Estimation of distribution algorithms|
|Series/Report no.:||Studies in Computational Intelligence, 613|
|Abstract:||The Targeted Estimation of Distribution Algorithm (TEDA) introduces into an EDA/GA hybrid framework a ‘Targeting’ process, whereby the number of active genes, or ‘control points’, in a solution is driven in an optimal direction. For larger feature selection problems with over a thousand features, traditional methods such as forward and backward selection are inefficient. Traditional EAs may perform better but are slow to optimize if a problem is sufficiently noisy that most large solutions are equally ineffective and it is only when much smaller solutions are discovered that effective optimization may begin. By using targeting, TEDA is able to drive down the feature set size quickly and so speeds up this process. This approach was tested on feature selection problems with between 500 and 20,000 features using all of these approaches and it was confirmed that TEDA finds effective solutions significantly faster than the other approaches.|
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