Summary of Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System, by M. Sajid et al.
Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System
by M. Sajid, M. Tanveer, P. N. Suganthan
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The ensemble deep random vector functional link (edRVFL) neural network has shown the ability to address limitations of conventional artificial neural networks. However, edRVFL’s feature generation through random projection can potentially lose intricate features or fail to capture certain non-linear features in its base models. To enhance feature learning capabilities, a novel edRVFL based on fuzzy inference system (edRVFL-FIS) is proposed. This model combines deep learning and ensemble approaches with the intrinsic IF-THEN properties of FIS, producing rich feature representation to train the ensemble model. The edRVFL-FIS incorporates diverse clustering methods (R-means, K-means, Fuzzy C-means) to establish fuzzy layer rules, resulting in three model variations: edRVFL-FIS-R, edRVFL-FIS-K, and edRVFL-FIS-C. Each base model uses original, hidden layer, and defuzzified features to make predictions. Experimental results across UCI and NDC datasets consistently demonstrate the superior performance of the proposed edRVFL-FIS models over baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new artificial intelligence model that combines two powerful techniques: deep learning and fuzzy logic. This model can learn from data in a way that’s more accurate and robust than previous approaches. The model uses special “fuzzy” rules to help it make decisions, which is useful when dealing with complex or uncertain data. The researchers tested their model on several datasets and found that it performed better than other models in many cases. This could be important for applications like medical diagnosis or financial forecasting. |
Keywords
» Artificial intelligence » Clustering » Deep learning » Ensemble model » Inference » K means » Neural network
