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| Title: | 貝氏相關 t 檢定之改進及其在交叉驗證資料中的應用
Enhancements to the Bayesian Correlated t-Test and Its Application to Cross-Validation Data |
| Authors: | 許育菱
Xu, Yu-Ling |
| Contributors: | 翁久幸
Weng, Chui-Hsing
許育菱
Xu, Yu-Ling |
| Keywords: | 交叉驗證
準確率
統計檢定
Correlated t-test
Bayesian correlated t-test
影像增強
圖像分類
Cross-validation
Accuracy
Statistical testing
Correlated t-test
Bayesian correlated t-test
Image enhancement
Image classification |
| Date: | 2024 |
| Issue Date: | 2024-09-04 14:57:11 (UTC+8) |
| Abstract: | 在統計學與機器學習任務中，交叉驗證(Cross-validation)是一種常見的方法，用於將原始數據集劃分為多個子集，使模型在不同的數據子集上反覆進行訓練和驗證。通過分析交叉驗證後產生的準確率資料，可以評估模型的效能和穩健性，亦能比較不同模型下的準確率差異。本研究針對交叉驗證資料進行統計檢定，使用了Correlated t-test、Bayesian correlated t-test，並提出修正Bayesian correlated t-test共變異數矩陣後的Bayesian correlated t-test2。使用模擬資料的研究結果顯示，Bayesian correlated t-test2在多數情況下表現優於Bayesian correlated t-test。而在結論上，Bayesian correlated t-test2與Correlated t-test十分相似，但是Bayesian correlated t-test2的優勢是能夠提供更多的額外資訊。此外，在實際資料分析上，本研究將Correlated t-test、Bayesian correlated t-test、Bayesian correlated t-test2用於比較影像增強方法對圖像分類表現的影響，發現在多數資料集中，Contrast Stretching處理後的分類結果較佳，而 Sharpening處理則相對較差。
In statistical and machine learning tasks, cross-validation is a common method used to divide the original dataset into multiple subsets, allowing the model to be trained and validated repeatedly on different subsets of the data. By analyzing the accuracy data generated from cross-validation, we can evaluate the model's performance and robustness, as well as compare the accuracy differences under different models. This study conducts a statistical testing of cross-validation data, utilizing the Correlated t-test and the Bayesian correlated t-test, and proposes the Bayesian correlated t-test2, which modifies the covariance matrix of the Bayesian correlated t-test. The results from simulated data show that the Bayesian correlated t-test2 outperforms the Bayesian correlated t-test in most cases. While the Bayesian correlated t-test2 is very similar to the Correlated t-test in conclusion, its advantage lies in providing additional information. Furthermore, in practical data analysis, this study applies the Correlated t-test, Bayesian correlated t-test, and Bayesian correlated t-test2 to compare the impact of image enhancement methods on image classification performance. It was found that, in most datasets, the classification results after Contrast Stretching treatment were better, while the results after Sharpening treatment were relatively poor. |
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| Description: | 碩士
國立政治大學
統計學系
111354026 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111354026 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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