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Reseach Article

Combination of PCA with SMOTE Resampling to Boost the Prediction Rate in Lung Cancer Dataset

by Mehdi Naseriparsa, Mohammad Mansour Riahi Kashani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 77 - Number 3
Year of Publication: 2013
Authors: Mehdi Naseriparsa, Mohammad Mansour Riahi Kashani
10.5120/13376-0987

Mehdi Naseriparsa, Mohammad Mansour Riahi Kashani . Combination of PCA with SMOTE Resampling to Boost the Prediction Rate in Lung Cancer Dataset. International Journal of Computer Applications. 77, 3 ( September 2013), 33-38. DOI=10.5120/13376-0987

@article{ 10.5120/13376-0987,
author = { Mehdi Naseriparsa, Mohammad Mansour Riahi Kashani },
title = { Combination of PCA with SMOTE Resampling to Boost the Prediction Rate in Lung Cancer Dataset },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 77 },
number = { 3 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 33-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume77/number3/13376-0987/ },
doi = { 10.5120/13376-0987 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:49:18.578266+05:30
%A Mehdi Naseriparsa
%A Mohammad Mansour Riahi Kashani
%T Combination of PCA with SMOTE Resampling to Boost the Prediction Rate in Lung Cancer Dataset
%J International Journal of Computer Applications
%@ 0975-8887
%V 77
%N 3
%P 33-38
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Classification algorithms are unable to make reliable models on the datasets with huge sizes. These datasets contain many irrelevant and redundant features that mislead the classifiers. Furthermore, many huge datasets have imbalanced class distribution which leads to bias over majority class in the classification process. In this paper combination of unsupervised dimensionality reduction methods with resampling is proposed and the results are tested on Lung-Cancer dataset. In the first step PCA is applied on Lung-Cancer dataset to compact the dataset and eliminate irrelevant features and in the second step SMOTE resampling is carried out to balance the class distribution and increase the variety of sample domain. Finally, Naïve Bayes classifier is applied on the resulting dataset and the results are compared and evaluation metrics are calculated. The experiments show the effectiveness of the proposed method across four evaluation metrics: Overall accuracy, False Positive Rate, Precision, Recall.

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Index Terms

Computer Science
Information Sciences

Keywords

PCA Irrelevant Features Unsupervised Dimensionality Reduction SMOTE.