A novel approach to find feature selection using modified fuzzy C-mean

Feature selection, also known as dimensionality reduction, is a common preprocessing step in the fields of pattern recognition, data mining, and machine learning. This is a critical issue when mining high-dimensional, massive data sets. Preprocessing the data before analysis to acquire a smaller collection of representative features and keeping the optimal salient properties of the data leads to more compactness of the models trained and better generalization, as well as a decrease in processing time. Therefore, the standard for dimension reduction is to save just the information that is most important from the original data, as determined by certain optimality criteria. In this paper we are going to form a new framework include the combination of MFCM and APSO, to find the HIDS.

• Publication year

  2023

• Venue

  World Journal of Advanced Engineering Technology and Sciences

• Publication date

  2023-09-30

• Fields of study

  Not labeled

• Identifiers
  DOI 10.30574/wjaets.2023.9.2.0224
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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