SSTop3: Sole-Top-Three and Sum-Top-Three Class Prediction Ensemble Method Using Deep Learning Classification Models

Abdulaziz Anorboev, Javokhir Musaev, Jeongkyu Hong, Ngoc Thanh Nguyen, Dosam Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Computer Vision (CV) has been employed in several different industries, with remarkable success in image classification applications, such as medicine, production quality control, transportation systems, etc. CV models rely on excessive images to train prospective models. Usually, the process of acquiring images is expensive and time-consuming. In this study, we propose a method that consists of multiple steps to increase image classification accuracy with a small amount of data. In the initial step, we set up multiple datasets from an existing dataset. Because an image carries pixel values between 0 and 255, we divided the images into pixel intervals depending on dataset type. If the dataset is grayscale, the pixel interval is divided into two parts, whereas it is divided into five intervals when the dataset consists of RGB images. In the next step, we trained the model using the original dataset and each created datasets separately. In the training process, each image illustrates a non-identical prediction space where we propose a top-three prediction probability ensemble method. Top-three predictions of newly generated images are ensemble to the corresponding probabilities of the original image. Results demonstrate that learning patterns from each pixel interval and ensemble the top three prediction vastly improves the performance and accuracy and the method can be applied to any model.

Original languageEnglish
Title of host publicationAdvances in Computational Collective Intelligence - 14th International Conference, ICCCI 2022, Proceedings
EditorsCostin Bădică, Jan Treur, Djamal Benslimane, Bogumiła Hnatkowska, Marek Krótkiewicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages193-199
Number of pages7
ISBN (Print)9783031162091
DOIs
StatePublished - 2022
Event14th International Conference on Computational Collective Intelligence, ICCCI 2022 - Hammamet, Tunisia
Duration: 28 Sep 202230 Sep 2022

Publication series

NameCommunications in Computer and Information Science
Volume1653 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference14th International Conference on Computational Collective Intelligence, ICCCI 2022
Country/TerritoryTunisia
CityHammamet
Period28/09/2230/09/22

Keywords

  • Classification task
  • Deep learning ensemble method
  • Image pixel interval

Fingerprint

Dive into the research topics of 'SSTop3: Sole-Top-Three and Sum-Top-Three Class Prediction Ensemble Method Using Deep Learning Classification Models'. Together they form a unique fingerprint.

Cite this