Cairo University, Cairo, Egypt.

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Abstract—Due to the need for the dimensionality reduction in many applications such as real-world and large scale applications, this paper demonstrates it, and covers it from all of its perspectives such as its components, its importance, and where it is needed. Moreover, this paper demonstrates the impact of irrelevant features on the performance of many predictors such as decision tree, Naïve Bayes, nearest neighbor, and support vector machines. This is because the performance of these predictors degenerates when provided with data containing irrelevant features.

Index Terms—Dimensionality reduction, feature selection, feature extraction, instance reduction.

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Cite: Nadia Abd-Alsabour, "On the Role of Dimensionality Reduction," Journal of Computers vol. 13, no. 5, pp. 571-579, 2018.