Code2vect: An efficient heterogenous data classifier and nonlinear regression technique - 03/12/19
, Ruben Ibáñez Pinillo a , Anais Barasinski b , Francisco Chinesta c, ⁎ Bienvenue sur EM-consulte, la référence des professionnels de santé.
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Abstract |
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.
Le texte complet de cet article est disponible en PDF.Keywords : Machine learning, Data representation, Classification, Categorial data, Neural network, High-dimensional data, Regression
Plan
Vol 347 - N° 11
P. 754-761 - novembre 2019 Retour au numéro
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