用特徵選擇和數據平衡對高維且分佈不均的二元資料做類別預測

dc.contributor呂翠珊zh_TW
dc.contributorLu, Tsui-Shanen_US
dc.contributor.author蘇立鴻zh_TW
dc.contributor.authorSu, Li-Hungen_US
dc.date.accessioned2023-12-08T07:55:59Z
dc.date.available2027-08-15
dc.date.available2023-12-08T07:55:59Z
dc.date.issued2022
dc.description.abstract近年來,機器學習 (ML) 在資料探勘和預測方面逐漸流行;與傳統的統計訓練相比,ML 有名的是在預測或分類數據方面的高準確度,但仍然存在一些限制。首先是如果資料的分布高度不平均,ML 算法會遇到準確度悖論,意思是說它只會對多數類別進行預測,我們使用採樣方法來解決這個問題。其次是面對高維資料時的計算時間,我們使用特徵選擇方法來解決這個問題。在前面的資料預處理之後,我們考慮四種 ML 算法:邏輯迴歸、K-近鄰 (KNN) 、隨機森林 (RF) 和極限梯度提升 (XGBoost) 來比較模型的性能。我們通過具有 687 個變數和 40041 個觀察值的醫療數據集急性腎損傷 (AKI) 演示了上述過程。主要結果是他們是否在 AKI 上復發。結果表明,XGBoost 在接受者操作特徵曲線下的面積 (AUC-ROC) 方面具有最佳性能。對於醫療數據集,鈉、速尿、芬太尼、布美他尼、多巴胺、胰島素、白蛋白、甘油和腎上腺素是最具影響力的藥物,CCS1581 是影響最大的疾病。zh_TW
dc.description.abstractIn the recent years, machine learning (ML) has become popular for data mining and predicting; compared to traditional statistical training, ML is known for its high accuracy on prediction or classifying the data. However, there still exists several limitations. First, if the distribution of the data is highly imbalanced, ML tends to meet accuracy paradox. To solve this problem, we use sampling methods. Second, dealing high dimensional data is computational demanding. We use feature selection methods to overcome this problem. After the aforementioned data preprocessing, we consider four ML algorithms: logistic regression, K-Nearest Neighbor (KNN), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to compare the performance of the model. We demonstrate the above procedure via a medical dataset Acute Kidney Injury (AKI) with 687 variables and 40041 observations. The main outcome is whether they have recurrence on AKI. The results shows that XGBoost has the best performance in terms of the area under the curve of receiver operating characteristic curve (AUC-ROC). For the medical dataset, Sodium, Furosemide, Fentanyl, Bumetanide, Dopamine, Insulin, Albumin, Glycerin and Epinephrine are top influential medication drugs and CCS1581 is top influential disease.en_US
dc.description.sponsorship數學系zh_TW
dc.identifier60940024S-41942
dc.identifier.urihttps://etds.lib.ntnu.edu.tw/thesis/detail/9be045c76d7ea756a663b94292667ad6/
dc.identifier.urihttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/121103
dc.language英文
dc.subject機器學習zh_TW
dc.subject邏輯迴歸zh_TW
dc.subjectK-近鄰zh_TW
dc.subject隨機森林zh_TW
dc.subject極限梯度提升zh_TW
dc.subject不平衡zh_TW
dc.subject準確率悖論zh_TW
dc.subject採樣zh_TW
dc.subject高維zh_TW
dc.subject特徵選擇zh_TW
dc.subjectmachine learningen_US
dc.subjectlogistic regressionen_US
dc.subjectK-Nearest Neighboren_US
dc.subjectRandom Foresten_US
dc.subjectExtreme Gradient Boostingen_US
dc.subjectimbalanceden_US
dc.subjectaccuracy paradoxen_US
dc.subjectsamplingen_US
dc.subjecthigh dimensionalen_US
dc.subjectfeature selectionen_US
dc.title用特徵選擇和數據平衡對高維且分佈不均的二元資料做類別預測zh_TW
dc.titleClass Prediction with Feature Selection and Data Balancing on High Dimensional and Imbalanced Binary Dataen_US
dc.typeetd

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