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You searched for subject:(Deep Rule Forest). Showing records 1 – 2 of 2 total matches.

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NSYSU

1. Kuo, Bo-Wen. Interpretable representation learning based on Deep Rule Forests.

Degree: Master, Information Management, 2018, NSYSU

The spirit of tree-based methods is to learn rules. A large number of machine learning techniques are tree-based. More complicated tree learners may result in higher predictive models, but may sacrifice for model interpretability. On the other hand, the spirit of representation learning is to extract abstractive concepts from manifestations of the data. For instance, Deep Neural networks (DNNs) is the most popular method in representation learning. However, unaccountable feature representation is the shortcoming of DNNs. In this paper, we proposed an approach, Deep Rule Forest (DRF), to learn region representations based on random forest in the deep layer-wise structures. The learned interpretable rules region representations combine other machine learning algorithms. We trained CART which learned from DRF region representations, and found that the prediction accuracies sometime are better than ensemble learning methods. Advisors/Committee Members: Keng-Pei Lin (chair), Yihuang, Kang (committee member), Pei-Ju, Lee (chair).

Subjects/Keywords: Rule Learning; Random Forest; Representation Learning; Interpretability; Deep Rule Forest

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kuo, B. (2018). Interpretable representation learning based on Deep Rule Forests. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kuo, Bo-Wen. “Interpretable representation learning based on Deep Rule Forests.” 2018. Thesis, NSYSU. Accessed June 16, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kuo, Bo-Wen. “Interpretable representation learning based on Deep Rule Forests.” 2018. Web. 16 Jun 2019.

Vancouver:

Kuo B. Interpretable representation learning based on Deep Rule Forests. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 Jun 16]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kuo B. Interpretable representation learning based on Deep Rule Forests. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


NSYSU

2. Huang, Ya-Jie. Detection of Drug-Disease Interactions for Acute Kidney Injury using Deep Rule Forests.

Degree: Master, Information Management, 2018, NSYSU

Patients with kidney diseases are often diagnosed with Acute Kidney Injury (AKI). The mortality rate of critically ill patients with AKI is 60%. As a result, if AKI is diagnosed earlier, patients may have greater chances to recover renal function, which will ultimately improve the patientsâ survival rate. The risk factors to AKI include drug-drug interactions and drug-disease interactions. According to previous researches, researchers used statistical analysis to measure the correlations between one disease and one drug. However, realistically, the correlations can be various when the patients usually have many prescriptions and complications. In this thesis, we propose a machine learning algorithm, Deep Rule Forests (DRF), which helps discover and extract rules from tree models as the combinations of drug and diseases usages to help identify aforementioned interactions. We also found that several drug and diseases usages that may be considered having significant impact on (re)occurrence of AKI. After that, the results show that DRF model performs better than typical tree-based and linear method in terms of the prediction accuracy. Moreover, we can obtain a series of situations that may cause AKI. If the layer of DRF model is higher, the extracted rules are more precise. Advisors/Committee Members: Yihuang Kang (committee member), Keng-Pei Lin (chair), Pei-Ju Li (chair).

Subjects/Keywords: Deep rule forests; Random forest; Drug-drug interactions; Acute kidney injury; Machine learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Huang, Y. (2018). Detection of Drug-Disease Interactions for Acute Kidney Injury using Deep Rule Forests. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716118-201801

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Huang, Ya-Jie. “Detection of Drug-Disease Interactions for Acute Kidney Injury using Deep Rule Forests.” 2018. Thesis, NSYSU. Accessed June 16, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716118-201801.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Huang, Ya-Jie. “Detection of Drug-Disease Interactions for Acute Kidney Injury using Deep Rule Forests.” 2018. Web. 16 Jun 2019.

Vancouver:

Huang Y. Detection of Drug-Disease Interactions for Acute Kidney Injury using Deep Rule Forests. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 Jun 16]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716118-201801.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Huang Y. Detection of Drug-Disease Interactions for Acute Kidney Injury using Deep Rule Forests. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716118-201801

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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