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Classification Preservation Using Assorted Dimensionality Reduction Techniques
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
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
How to cite this paper
@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},
}