Reinforced Dual-Flow Neural Network for Tabular Data Classification With Dynamical Transformer and Fuzzy Clustering
Why this work is in the frame
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Bibliographic record
Abstract
A novel reinforced dual-flow neural network based on attention and a polynomial-based radial basis function network (DFBTP) is proposed to enhance classification performance on tabular data. DFBTP consists of two types of architectures such as advanced Transformer model based on Tabular Prior data Fitted Network (TabPFN) and SV-clustering driven radial basis function neural network (SV-PRBFNN). The conventional PRBFNN model may encounter local minima and noise issues during training, which negatively impacts its performance. Local minima result from initial parameter choices, and noise issues are inherent in the dataset. By introducing the Transformer model and the Whale Optimization Algorithm (WOA), these challenges can be mitigated. Within the dual-flow architecture, the attention flow is trained using Bayesian inference capabilities and structural causal models, and it uses the self-attention mechanism to capture global features. This approach mitigates the problem of local minima. SV-PRBFNN flow uses the fuzzy clustering algorithm based on support vectors to replace the original radial basis function for training. Fuzzy clustering based on support vectors can alleviate the negative impact of outliers on model performance and also reduce the number of fuzzy rules. During neural network hyperparameter optimization, WOA is used to identify the global optimal values for hyperparameters. In the experiments, DFBTP demonstrated its superiority in classification accuracy in comparative experiments on 18 datasets and 13 models, and also performed well on real-world datasets. The robust performance of DFBTP was further validated through statistical analysis of the experimental results.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it