Abstract
The recent increase of dimensionality of data is a target for many existing feature selection methods with respect to efficiency and effectiveness. In this paper, the all relevant feature selection method based on information gathered using generational feature elimination was introduced. The successive generations of feature subset were defined using DTLevelImp algorithm and in each step the subset of most important features were eliminated from the primary investigated dataset. This process was executed until the most important feature reach importance value on the level similar to importance of the random shadow features. The proposed method was also initially tested on well-know artificial and real-world datasets and the results confirm its efficiency. Thus, it can be concluded that selected attributes are relevant.
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Acknowledgment
This work was supported by the Center for Innovation and Transfer of Natural Sciences and Engineering Knowledge at the University of Rzeszów.
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Paja, W. (2018). Generational Feature Elimination to Find All Relevant Feature Subset. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-59421-7_13
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DOI: https://doi.org/10.1007/978-3-319-59421-7_13
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