Abstract
The increased dimensionality of genomic and proteomic data produced by microarray and mass spectrometry technology makes testing and training of general classification method difficult. Special data analysis is demanded in this case and one of the common ways to handle high dimensionality is identification of the most relevant features in the data. Wrapper feature selection is one of the most common and effective techniques for feature selection. Although efficient, wrapper methods have some limitations due to the fact that their result depends on the search strategy. In theory when a complex search is used, it may take much longer to choose the best subset of features and may be impractical in some cases. Hence we propose a new wrapper feature selection for big data based on a random search using genetic algorithm and prior information. The new approach was tested on 2 biological dataset and compared to two well known wrapper feature selection approaches and results illustrate that our approach gives the best performances.