Neural Network Feature Explanation Using Neuron Activation Rate Based Bipartite Graph Article Swipe
Deepthi Praveenlal Kuttichira
,
Basim Azam
,
Brijesh Verma
,
Ashfaqur Rahman
,
Lipo Wang
,
Abdul Sattar
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/ivcnz61134.2023.10343968
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/ivcnz61134.2023.10343968
Deep Neural Networks (DNNs) are popular machine learning models that have gained popularity due to its good predictive accuracy and ability to automatically learn features from raw data. Convolutional Neural Networks (CNNs) are one such models that have gained popularity in the field of Computer Vision (CV). Despite the popularity, these models are notoriously black-box models. The decisions made by these models are not explainable. In this paper we propose a method to create a Neuron Activation Rate based Bipartite Graph (NARBG) , that can explain the decisions made by the model, based on the contributions of class specific features. In the proposed method, the features are extracted from the raw data using a CNN based architecture. From the extracted features, neuron activation rate is calculated. Based on these neuron activation rates, influential features for the target class prediction are identified. Then a bipartite graph named NARBG is trained using these influential features. The predictions of NARBG can be explained based on the features and the path in the graph that got activated for a given target class prediction. The proposed method performs on par with the other state-of-the-art methods in terms of accuracy.
Related Topics
Concepts
Bipartite graph
Computer science
Artificial intelligence
Popularity
Graph
Convolutional neural network
Artificial neural network
Feature extraction
Pattern recognition (psychology)
Feature (linguistics)
Deep learning
Machine learning
Theoretical computer science
Social psychology
Philosophy
Linguistics
Psychology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ivcnz61134.2023.10343968
- OA Status
- green
- Cited By
- 1
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389628733
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389628733Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/ivcnz61134.2023.10343968Digital Object Identifier
- Title
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Neural Network Feature Explanation Using Neuron Activation Rate Based Bipartite GraphWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-11-29Full publication date if available
- Authors
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Deepthi Praveenlal Kuttichira, Basim Azam, Brijesh Verma, Ashfaqur Rahman, Lipo Wang, Abdul SattarList of authors in order
- Landing page
-
https://doi.org/10.1109/ivcnz61134.2023.10343968Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/10072/429717Direct OA link when available
- Concepts
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Bipartite graph, Computer science, Artificial intelligence, Popularity, Graph, Convolutional neural network, Artificial neural network, Feature extraction, Pattern recognition (psychology), Feature (linguistics), Deep learning, Machine learning, Theoretical computer science, Social psychology, Philosophy, Linguistics, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2023-11-29 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2163922914, https://openalex.org/W2336525064, https://openalex.org/W3199173685, https://openalex.org/W6784491624, https://openalex.org/W4287027946, https://openalex.org/W3093687066, https://openalex.org/W6632100814, https://openalex.org/W3116286104, https://openalex.org/W6780484765, https://openalex.org/W3135613089, https://openalex.org/W4225913583, https://openalex.org/W2962858109, https://openalex.org/W6790754042, https://openalex.org/W6849483933, https://openalex.org/W3045825034, https://openalex.org/W6847230921, https://openalex.org/W2764024122, https://openalex.org/W6802820957, https://openalex.org/W4312048199, https://openalex.org/W4287640226, https://openalex.org/W1538131130, https://openalex.org/W3211363806, https://openalex.org/W4287333021, https://openalex.org/W3037626499, https://openalex.org/W4320086284 |
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