Explainable Decision Forest - Revision history

Slawek at 20:18, 24 August 2024

2024-08-24T20:18:38Z

Slawek: Reverted edits by Slawek (talk) to last revision by Hamid

2024-08-24T20:18:05Z

Reverted edits by Slawek (talk) to last revision by Hamid

Slawek at 20:16, 24 August 2024

2024-08-24T20:16:45Z

Hamid at 13:01, 4 September 2023

2023-09-04T13:01:17Z

Hamid: Created page with "{{StudentProjectTemplate |Summary=Designing an explainable decision forest classifier for fault detection |Keywords=decision forest, explainable AI, fault detection |TimeFrame..."

2023-09-04T11:26:04Z

Created page with "{{StudentProjectTemplate |Summary=Designing an explainable decision forest classifier for fault detection |Keywords=decision forest, explainable AI, fault detection |TimeFrame..."

New page

{{StudentProjectTemplate
|Summary=Designing an explainable decision forest classifier for fault detection
|Keywords=decision forest, explainable AI, fault detection
|TimeFrame=Autumn 2023
|Supervisor=Hamid Sarmadi, Sepideh Pashami, Sławomir Nowaczyk,
|Status=Open
}}
An algorithm to train separate "explainable" decision trees for detecting different types of fault has been developed. We would like to extend the algorithm to an ensemble (Decision Forest) method when decision trees are aware of each other.

You can read more about the original algorithm in the following link: https://hhse-my.sharepoint.com/:b:/g/personal/hamid_sarmadi_hh_se/EVv0c-uonSxHhHyf93N5yPsBEp79kUXLCP6e2hQKBvRF9Q?e=3CscLX

@@ Line 1: / Line 1: @@
 {{StudentProjectTemplate
-|Summary=Designing a deep model that uses decision trees instead of artificial neurons
+|Summary=Designing an explainable decision forest classifier for fault detection
-|Keywords=deep decision forest, explainable AI
+|Keywords=decision forest, explainable AI, fault detection
-|TimeFrame=Autumn 2024
+|TimeFrame=Autumn 2023
-|Supervisor=Sławomir Nowaczyk,
+|Supervisor=Hamid Sarmadi, Sepideh Pashami, Sławomir Nowaczyk,
 |Status=Open
 }}
-The success of Deep Learning is largely attributed to the ability to create/extract "hierarchical features" from the data. This is successful using artificial neurons or perceptrons, and the backpropagation algorithm.
+An algorithm to train separate "explainable" decision trees for detecting different types of fault has been developed. We would like to extend the algorithm to an ensemble (Decision Forest) method when decision trees are aware of each other.
-The price is, however, the very large size of the model, which translates into computational costs and a "black-box" nature, or lack of explainability.
+You can read more about the original algorithm in the following link: https://hhse-my.sharepoint.com/:b:/g/personal/hamid_sarmadi_hh_se/EWlwNDHNrnNMqBQ93dNdu9kBS61tWwF56a-rI7A-kPEpRA?e=EpEFQn

@@ Line 1: / Line 1: @@
 {{StudentProjectTemplate
-|Summary=Designing an explainable decision forest classifier for fault detection
+|Summary=Designing a deep model that uses decision trees instead of artificial neurons
-|Keywords=decision forest, explainable AI, fault detection
+|Keywords=deep decision forest, explainable AI
-|TimeFrame=Autumn 2023
+|TimeFrame=Autumn 2024
-|Supervisor=Hamid Sarmadi, Sepideh Pashami, Sławomir Nowaczyk,
+|Supervisor=Sławomir Nowaczyk,
 |Status=Open
 }}
-An algorithm to train separate "explainable" decision trees for detecting different types of fault has been developed. We would like to extend the algorithm to an ensemble (Decision Forest) method when decision trees are aware of each other.
+The success of Deep Learning is largely attributed to the ability to create/extract "hierarchical features" from the data. This is successful using artificial neurons or perceptrons, and the backpropagation algorithm.
-You can read more about the original algorithm in the following link: https://hhse-my.sharepoint.com/:b:/g/personal/hamid_sarmadi_hh_se/EWlwNDHNrnNMqBQ93dNdu9kBS61tWwF56a-rI7A-kPEpRA?e=EpEFQn
+The price is, however, the very large size of the model, which translates into computational costs and a "black-box" nature, or lack of explainability.

← Older revision		Revision as of 13:01, 4 September 2023
Line 8:		Line 8:
	An algorithm to train separate "explainable" decision trees for detecting different types of fault has been developed. We would like to extend the algorithm to an ensemble (Decision Forest) method when decision trees are aware of each other.		An algorithm to train separate "explainable" decision trees for detecting different types of fault has been developed. We would like to extend the algorithm to an ensemble (Decision Forest) method when decision trees are aware of each other.

−	You can read more about the original algorithm in the following link: https://hhse-my.sharepoint.com/:b:/g/personal/hamid_sarmadi_hh_se/~~EVv0c~~-~~uonSxHhHyf93N5yPsBEp79kUXLCP6e2hQKBvRF9Q~~?e=~~3CscLX~~	+	You can read more about the original algorithm in the following link: https://hhse-my.sharepoint.com/:b:/g/personal/hamid_sarmadi_hh_se/EWlwNDHNrnNMqBQ93dNdu9kBS61tWwF56a-rI7A-kPEpRA?e=EpEFQn