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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Tracking The Sensitivity of The Learning Models Toward Exact and Near Duplicates

Author 1: Menna Ibrahim Gabr
Author 2: Yehia Helmy
Author 3: Doaa S. Elzanfaly

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0131240

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 12, 2022.

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Abstract: Most real-world datasets contaminated by quality issues have a severe effect on the analysis results. Duplication is one of the main quality issues that hinder these results. Different studies have tackled the duplication issue from different perspectives. However, revealing the sensitivity of supervised and unsupervised learning models under the existence of different types of duplicates, deterministic and probabilistic, is not broadly addressed. Furthermore, a simple metric is used to estimate the ratio of both types of duplicates regardless of the probability by which the record is considered duplicate. In this paper, the sensitivity of five classifiers and four clustering algorithms toward deterministic and probabilistic duplicates with different ratios (0% - 15%) is tracked. Five evaluation metrics are used to accurately track the changes in the sensitivity of each learning model, MCC, F1-Score, Accuracy, Average Silhouette Coefficient, and DUNN Index. Also, a metric to measure the ratio of probabilistic duplicates within a dataset is introduced. The results revealed the effectiveness of the proposed metric that reflects the ratio of probabilistic duplicates within the dataset. All learning models, classification, and clustering models are differently sensitive to the existence of duplicates. RF and Kmeans are positively affected by the existence of duplicates which means that their performce increase as the percentage of duplicates increases. Furthermore, the rest of classifiers and clustering algorithms are sensitive toward duplicates existence, especially within high percentage that negatively affect their performance.

Keywords: Deduplication; deterministic duplicates; probabilistic duplicates; supervised learning models; unsupervised learning models; evaluation metrices

Menna Ibrahim Gabr, Yehia Helmy and Doaa S. Elzanfaly, “Tracking The Sensitivity of The Learning Models Toward Exact and Near Duplicates” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131240

@article{Gabr2022,
title = {Tracking The Sensitivity of The Learning Models Toward Exact and Near Duplicates},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131240},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131240},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Menna Ibrahim Gabr and Yehia Helmy and Doaa S. Elzanfaly}
}


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