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Feature reduction for simplified handwritten digit classification model

  • Silesian University of Technology

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

1 Citation (Scopus)

Abstract

In this paper we present a model for feature reduction of handwritten digits in simple classifiers and comparison of performance of said classifiers. Classification will be done on modified databases of 28x28 px images containing handwritten numbers from 0 to 9. Modifications in databases were obtained by creating vectors from pixels, also later modified by using PCA and in some cases down-sampling. Comparison of KNN, Naive Bayes, Fuzzy Clustering, Centroids - McQueen classifiers will be done, based on standard metrics and process time.

Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
EditorsHisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages448-454
Number of pages7
ISBN (Electronic)9781665487689
DOIs
Publication statusPublished - 2022
Event2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 - Singapore, Singapore
Duration: 4 Dec 20227 Dec 2022

Publication series

NameProceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

Conference

Conference2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Country/TerritorySingapore
CitySingapore
Period4/12/227/12/22

Keywords

  • Centroids - McQueen
  • Classification
  • Fuzzy Clustering
  • KNN
  • MNIST
  • Naive Bayes
  • Number Recognition
  • Python

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)
  • Computational Mathematics
  • Control and Optimization
  • Transportation

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