Abstract
High Dimensional Data (HDD) is one of the biggest challenges in Data Science arising from Big Data. The application of dimensionality reduction techniques over HDD allows visualization and, thus, a better problem understanding. In addition, these techniques also can enhance the performance of Machine Learning (ML) algorithms while increasing the explanatory power. This paper presents an automatic method capable of obtaining an adequate representation of the data, given a previously trained ML model. Likewise, an automatic method is introduced to bring a Support Vector Machine (SVM) model based on an adequate representation of the data. Both methods provide an Explanaible Machine Learning procedure. The proposal is tested on several data sets providing promising results. It significantly eases the visualization and understanding task to the data scientist when a ML model has already been trained, as well as the ML selection parameters when a reduced representation of data has been achieved.
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Acknowledgements
This research has been supported by grants from Rey Juan Carlos University (Ref: C1PREDOC2020), Madrid Autonomous Community (Ref: IND2019/TIC-17194) and the Spanish Ministry of Economy and Competitiveness, under the Retos-Investigación program: MODAS-IN (Ref: RTI-2018-094269-B-I00).
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Cuesta, M., Martín de Diego, I., Lancho, C., Aceña, V., M. Moguerza, J. (2021). From Classification to Visualization: A Two Way Trip. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2021. IDEAL 2021. Lecture Notes in Computer Science(), vol 13113. Springer, Cham. https://doi.org/10.1007/978-3-030-91608-4_29
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DOI: https://doi.org/10.1007/978-3-030-91608-4_29
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