TINTO: Converting Tidy Data into image for classification with 2-Dimensional Convolutional Neural Networks

Manuel Castillo-Cara, Reewos Talla-Chumpitaz, Raúl García-Castro, Luis Orozco-Barbosa

Research output: Contribution to journalArticle (Contribution to Journal)peer-review

1 Scopus citations

Abstract

The growing interest in the use of algorithms-based machine learning for predictive tasks has generated a large and diverse development of algorithms. However, it is widely known that not all of these algorithms are adapted to efficient solutions in certain tidy data format datasets. For this reason, novel techniques are currently being developed to convert tidy data into images with the aim of using Convolutional Neural Networks (CNNs). TINTO offers the opportunity to convert tidy data into images through the representation of characteristic pixels by implementing two dimensional reduction algorithms: Principal Component Analysis (PCA) and t-distributed Stochastic Neighbour Embedding (t-SNE). Our proposal also includes a blurring technique, which adds more ordered information to the image and can improve the classification task in CNNs.

Translated title of the contributionTINTO: Conversión de datos ordenados en imagen para su clasificación con redes neuronales convolucionales bidimensionales
Original languageEnglish
Article number101391
JournalSoftwareX
Volume22
DOIs
StatePublished - May 2023
Externally publishedYes

Keywords

  • Convolutional neural networks
  • Image blurring technique
  • Image classification
  • Image generation
  • Tabular data into image
  • Tabular to image conversion

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