Fusion of Explainable Deep Learning Features Using Fuzzy Integral in Computer Vision
Bibliographic record
Abstract
Fuzzy integral fusion has been shown as an effective tool for enhancing classification accuracy while also achieving explainability. With the deep learning boom in the past decade, many researchers have investigated the advantages of fusing various deep neural networks (DNNs) with fuzzy integral techniques in computer vision. However, recent studies focus on only the explainable fusion process. Thus, features learned by each DNN are difficult to understand. Moreover, DNNs are usually trained on the ImageNet dataset, whereas the effectiveness of applying the fuzzy integral methods to this dataset is yet to be investigated. This is the gap that motivates our research study. To address this issue, we explore fuzzy integral fusion classification models that make both the fusion process and extracted features explainable. Specifically, we use two well-known fuzzy integral fusion methods [i.e., Sugeno integral (SI) and Choquet integral (ChI)] to combine three explainable deep learning features (i.e., shape, texture, and color) in a manner that mimics the human visual recognition process. The originality of our work includes the emphasis on complete explainability in the classification process, the investigation of applying fuzzy integral methods to the ImageNet dataset, and extensive experimental validation of the effectiveness of fuzzy integral. Computational experiments show that fuzzy integral fusion can improve classification accuracy by 14.6% compared with an individual DNN on subsets derived from the ImageNet dataset. Furthermore, fuzzy integral fusion helps understand contributions, relationships, and interactions among the three features (shape, texture, and color) for each class, providing convincing evidence for the final classification result. Consequently, the proposed models not only achieve impressive performance, but also provide a thorough understanding of how these models work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".