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|Title:||Feature Selection via Chaotic Antlion Optimization|
|Citation:||PLOS ONE,11 (3): pp. e0150652 - e0150652, (2016)|
|Abstract:||Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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