Investigation the Impact of Data Pre-Processing and Dimensionality Reduction to Improve the Accuracy of Machine Learning
DOI:
https://doi.org/10.18486/ijcsnt/14.2.006Keywords:
Data Pre-Processing, Min-Max, Z score, Decimal Scaling, Log2, PCA, LDA, J48Abstract
Data pre-processing stands as a fundamental stage in machine learning and data mining. Within this domain, normalization, discretization, and dimensionality reduction are widely recognized techniques. This research paper aims to investigate the impact of Min-max, Z-score, Decimal Scaling, and Logarithm to the base 2 on the accuracy of the J48 classifier, utilizing the NSL-KDD dataset. Experiments were conducted employing these methods, and their respective outcomes were systematically compared. Additionally, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were assessed for dimensionality reduction. Notably, a hybrid combination of PCA and LDA was explored, demonstrating an enhanced classification accuracy compared to the individual methods.
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