Evolving Feedforward Neural Networks Using a Quasi-Opposition-Based Differential Evolution for Data Classification
Bibliographic record
Abstract
One of the most challenging problems in conducting machine learning is the learning process of feedforward neural networks (FFNN), which means finding the proper weights for connections and biases. The performance of FFNNs is mainly dependent on the success of the learning process. Gradient descent-based methods such as back-propagation (BP) are among the most widely employed learning algorithms, whereas they are susceptible to be trapped in local optima. Population-based metaheuristic algorithms such as differential evolution (DE) are a reliable alternative to tackle complex problems. In this paper, we propose a quasi-opposition-based differential evolution approach for FFNN learning to improve the performance of FFNNs (QODE-FFNN). Our proposed algorithm benefits from a variant of opposition-based learning (OBL) to enhance the performance of FFNN. Based on OBL concept, the opposite of a candidate solution is generated. Afterward, OBL selects the best between a candidate solution and its opposite based on their objective function values. In this paper, we employed a variant of OBL to improve the performance of FFNN, called quasi-OBL, which generates a random point between the center of search space and its opposite. Also, in our proposed algorithm, connection weights and biases are encoded as a candidate solution, while the objective function is based on a classification error. Experimental results confirm the performance of QODE-FFNN compared to other recent approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".