| _version_ |
1860800044149506048
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| building |
INTELEK Repository
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| collection |
Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2020-08-13 04:29:12
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| eventvenue |
Riyadh; Saudi Arabia
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| format |
Restricted Document
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| id |
8421
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| institution |
UniSZA
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| originalfilename |
1819-01-FH03-FIK-20-39681.pdf
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| person |
like Gecko) Chrome/84.0.4147.125 Safari/537.36
Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML
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| recordtype |
oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=8421
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| spelling |
8421 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=8421 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper application/pdf 7 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in like Gecko) Chrome/84.0.4147.125 Safari/537.36 Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML 2020-08-13 04:29:12 1819-01-FH03-FIK-20-39681.pdf UniSZA Private Access Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique Imbalanced datasets typically occur in many real applications. Resampling is one of the effective solutions due to producing a balanced class distribution. Synthetic Minority Over-sampling technique (SMOTE), an over-sampling technique is used in this study for dealing the imbalanced dataset by add the number of instances of a minority class. This technique is used to decrease the imbalance percentage of the dataset by generating new synthetic samples. Thus, a balanced training dataset is produced to replace the class imbalanced . The balanced datasets were obtained and trained with machine learning algorithms to diagnose the disease’s class. Through the experiment findings on the real-world datasets, oral cancer dataset and erythemato-squamous diseases dataset from the UCI machine learning datasets, an over-sampling method showed better results in clinical disease classification. 1st International Conference on Intelligent Cloud Computing, ICC 2019 Riyadh; Saudi Arabia
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| spellingShingle |
Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique
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| summary |
Imbalanced datasets typically occur in many real applications. Resampling is one of the effective solutions due to producing a balanced class distribution. Synthetic Minority Over-sampling technique (SMOTE), an over-sampling technique is used in this study for dealing the imbalanced dataset by add the number of instances of a minority class. This technique is used to decrease the imbalance percentage of the dataset by generating new synthetic samples. Thus, a balanced training dataset is produced to replace the class imbalanced . The balanced datasets were obtained and trained with machine learning algorithms to diagnose the disease’s class. Through the experiment findings on the real-world datasets, oral cancer dataset and erythemato-squamous diseases dataset from the UCI machine learning datasets, an over-sampling method showed better results in clinical disease classification.
|
| title |
Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique
|
| title_full |
Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique
|
| title_fullStr |
Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique
|
| title_full_unstemmed |
Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique
|
| title_short |
Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique
|
| title_sort |
improving accuracy of imbalanced clinical data classification using synthetic minority over-sampling technique
|