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博碩士論文 etd-0814122-092722 詳細資訊
Title page for etd-0814122-092722
論文名稱
Title
可解釋的表徵學習 - 使用基於模型的深度規則森林
Interpretable Representation Learning with Model-based Deep Rule Forest
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
64
研究生
Author
指導教授
Advisor
召集委員
Convenor
李珮如
LEE, PEI-JU
口試委員
Advisory Committee
楊惠芳
Yang,Huei-Fang
口試日期
Date of Exam
2022-07-29
繳交日期
Date of Submission
2022-09-14
關鍵字
Keywords
規則學習、可解釋性、機器學習、深度規則森林、深度模型結構
Rule Learning, Interpretability, Machine Learning, Deep Rule Forest, Deep Model Architecture
統計
Statistics
本論文已被瀏覽 149 次,被下載 38
The thesis/dissertation has been browsed 149 times, has been downloaded 38 times.
中文摘要
受益於運算成本的下降,深度學習模型被廣泛應用到許多任務中。雖然這類模型在預測上的表現比傳統機器學習模型改善許多,但由於這類模型的模型結構過於複雜,使得人們無法理解模型的決策過程,資料中隱含的歧視可能被模型學習到且無法被辨識出來,因此出現法律要求模型具有可解釋性。然而相比於深度學習模型,目前具有可解釋性的模型無法從資料中學到比較複雜的特徵,因此預測表現很難與深度學習模型相比。為了在學習到複雜的特徵的同時也能具有可解釋性,我們從基於模型的隨機森林中萃取出規則與其中的有母數模型的參數,並且加深模型結構來學習到更複雜的特徵。我們提出基於模型的深度規則森林,結合可解釋性與深層的模型架構來使模型兼具可解釋性與較好的預測表現,同時結合模型中的有母數模型的係數,來比較不同規則中的有母數模型的變數關係。
Abstract
Deep learning models are widely applied to many fields due to the decrease in computation cost. Although the performance of such models in prediction is much better than traditional machine learning models, the complexity of the model structure of such models makes it impossible to understand the decision process of the models. The implicit discrimination in the data may be learned by the models and cannot be recognized, so there is a legal requirement for the models to be interpretable. However, current models with interpretability cannot learn more complex features from the data than deep learning models, so the prediction performance is hardly comparable to deep learning models. To learn complex features while being interpretable, we extract the rules from the model-based random forest with the parameters of the parent model and deepen the model structure to learn more complex features. We propose a model-based Deep Rule Forest (mobDRF) that combines interpretability with a deep model structure to make the model interpretable and better predictive. We combine the coefficients of the parent model in the model to compare the relationship between the variables of the parent model in different rules.
目次 Table of Contents
論文審定書i
摘要ii
Abstractiii
List of Figuresv
List of Tablesvi
1. Introduction1
2. Background3
2.1 Representation Learning3
2.2 Interpretable Machine Learning4
2.3 Random Forests with Deep Architecture6
2.4 Model-based Recursive Partitioning7
3. Using Model-based Deep Rule Forests9
3.1 Building Model-based DRF10
3.2 Rule Interpretation for Model-based DRF12
4. Experiment13
4.1 Experiment Setup13
4.2 Model Performance Comparison15
4.3 Model Interpretation with Learned Rules19
5. Conclusion and Discussion24
References26
Appendix32
Appendix A. Rules of Care Home Incidents32
Table A1. Rule of rpart32
Table A2. Rule of C5034
Table A3. Rule of PRE37
Table A4. Rule of MOB tree40
Table A5. Rule of MOB tree with 1st layer of mobDRF41
Appendix B. Rules of TLSA 201545
Table B1. Rule of rpart45
Table B2. Rule of MOB tree47
Table B3. Rule of PRE49
Table B4. Rule of MOB tree with 1st layer of mobDRF51
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