Comptes Rendus
no. 11
Code2vect: An efficient heterogenous data classifier and nonlinear regression technique
Clara Argerich Martín1; Ruben Ibáñez Pinillo1; Anais Barasinski2; Francisco Chinesta3 
1 PIMM, Arts et Métiers Institute of Technology, CNRS, CNAM, HESAM University, 151, boulevard de l'Hôpital, 75013 Paris, France
2 University of Pau & Pays Adour, E2S UPPA, IPREM UMR5254, 64000 Pau, France
3 ESI GROUP Chair @ PIMM, Arts et Métiers Institute of Technology, 151, boulevard de l'Hôpital, 75013 Paris, France
Comptes Rendus. Mécanique, Data-Based Engineering Science and Technology, Volume 347 (2019) no. 11, pp. 754-761.

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.

Received:
Accepted:
Published online:
DOI: 10.1016/j.crme.2019.11.002
Mots-clés : Machine learning, Data representation, Classification, Categorial data, Neural network, High-dimensional data, Regression

Clara Argerich Martín 1; Ruben Ibáñez Pinillo 1; Anais Barasinski 2; Francisco Chinesta 3

1 PIMM, Arts et Métiers Institute of Technology, CNRS, CNAM, HESAM University, 151, boulevard de l'Hôpital, 75013 Paris, France
2 University of Pau & Pays Adour, E2S UPPA, IPREM UMR5254, 64000 Pau, France
3 ESI GROUP Chair @ PIMM, Arts et Métiers Institute of Technology, 151, boulevard de l'Hôpital, 75013 Paris, France 
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     title = {\protect\emph{Code2vect}: {An} efficient heterogenous data classifier and nonlinear regression technique},
     journal = {Comptes Rendus. M\'ecanique},
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Clara Argerich Martín; Ruben Ibáñez Pinillo; Anais Barasinski; Francisco Chinesta. Code2vect: An efficient heterogenous data classifier and nonlinear regression technique. Comptes Rendus. Mécanique, Data-Based Engineering Science and Technology, Volume 347 (2019) no. 11, pp. 754-761. doi : 10.1016/j.crme.2019.11.002. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2019.11.002/

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