Classification Preservation Using Assorted Dimensionality Reduction Techniques

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@article{1704964,
      author = {Usman A. Baba, Augustine S. Nsang},
      title = {Classification Preservation Using Assorted Dimensionality Reduction Techniques},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {2},
      pages = {245-257},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704964.pdf},
      abstract = {In this paper, we implement the perceptron classification algorithm and apply it to three two-class datasets which include the student, weather and ionosphere datasets. Then the k-Nearest Neighbors classification algorithm is also applied to the same two-class datasets. Each dataset is then reduced using fourteen different dimensionality reduction techniques. The perceptron and k-nearest neighbor classification algorithms are then applied to each reduced set and the performances of the dimensionality reduction techniques in preserving the classification of a dataset by the k-nearest neighbors and perceptron classification algorithm are compared. The extent to which the classification of a dataset is preserved by a given dimensionality reduction technique is evaluated using the rand index and confusion matrices.},
      keywords = {Classification, Confusion Matrix, Dimensionality Reduction, Eager Learner, k-Nearest Neighbors, Lazy Learner, Perceptron, Rand Index},
      month = {August},
  }