Abstract
Variable selection is an important concept in data mining, which can improve both the performance of machine learning and the process knowledge by removing the irrelevant and redundant features. The paper presents a hybrid variable selection approach that merges a combination of filters with a wrapper in order to obtain an informative subset of variables in a reasonable time, improving the stability of the single approach of more than 36% in average, without decreasing the system performance. The proposed method is tested on datasets coming from the UCI repository and from industrial contexts.
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Cateni, S., Colla, V. (2018). A Hybrid Variable Selection Approach for NN-Based Classification in Industrial Context. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_17
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DOI: https://doi.org/10.1007/978-3-319-56904-8_17
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