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博碩士論文 etd-0804120-103028 詳細資訊
Title page for etd-0804120-103028
論文名稱
Title
探索性多模態機器學習模型—以房產鑑價為例
Exploration of Multimodal Machine Learning Model - Findings from Real Estate Valuation
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
46
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2020-08-28
繳交日期
Date of Submission
2020-09-04
關鍵字
Keywords
房產鑑價、模型可解釋性、多模態模型、卷積神經網路
Convolutional neural network, Real estate value evaluation, Multi-modal model, Model interpretability
統計
Statistics
本論文已被瀏覽 6135 次,被下載 106
The thesis/dissertation has been browsed 6135 times, has been downloaded 106 times.
中文摘要
機器學習以及深度學習近年來被廣泛應用在各個領域,然而許多模型在追求預 測表現的同時卻犧牲了模型的可解釋性,使得模型像是黑盒子一樣讓人難以理解。在 本文中,我們提出透過多模態模型的概念來使得模型同時擁有預測準確度以及模型的 可解釋性。模型的可解釋性指得是我們能不能去了解模型是如何產生預測結果的,或 者是說模型是根據哪些特徵來產生預測。我們透過房地產鑑價為例子並搭配我們所提 出的多模態模型架構來驗證我們的想法,使得模型在擁有好的預測表現的同時,也具 有一定的解釋能力。而模型的可解釋性我們更進一步透過模型所學到的特徵來做全局 解釋以及透過局部解釋器來做局部解釋。
Abstract
Machine learning and deep learning have been woidely used in various fields in recent years. However, many models sacrifice the model interpretability while purchasing the predictive performance, which make the model difficult to understand like a block box. In this article, we propose the concept of multi-modal models to enable the model to have both predictive performance and model interpretability. The interpretability of a model refers to whether we can understand how the model produces the predictions. We take real estate value evaluation task as an example with our propsed method yp verify our ideas, so that the model has a good predictive performance while also having a certain explanatory power. As for the interpretability of the model, we further use the features learned by the model to make a global explanations and a local explainer to make a local explanations.
目次 Table of Contents
論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
List of Figures vi
List of Tables vii
1. Introduction 1
2. Background & Related Work 3
2.1. Explainable AI 3
2.2. Housing Price Estimation 6
2.3. Representation Learning 8
2.4. Multi-modal Model 10
2.5. LIME explainer 11
3. Methodology 13
3.1. Real estate transaction dataset 14
3.2. Boosting model’s predictive performance by image features 14
3.3. Interpretability of model 17
3.3.1. Explaining the models by sets of labels 18
3.3.2. Explaining the models by LIME explainer 19
4. Experimental Results 22
4.1. Experiment environment 22
4.2. Data pre-processing 23
4.3. The base real estate value evaluation model 25
4.4. The effect of images’ embeddings 26
4.5. Model interpretability 28
4.5.1. Explain the models by images’ labels 28
4.5.2. Explain the models by LIME explainer 30
5. Conclusion 35
6. Reference 35
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