A Novel Neural Network-Based Approach for Multiple Instance Learning
Bibliographic record
Abstract
This paper proposes a new multiple instance learning (MIL) method based on a MIL back-propagation neural network (MIBP), which is an extension of the standard back-propagation neural network (BPNN) that uses labeled bags of instances as training data. The method finds a concept point t in the feature space which is close to instances from positive bags and far from instances in negative bags. Our method is as follows: First, train MIBP with positive and negative bags. Second, extract t from the trained MIBP. This is achieved by, for each positive bag, presenting all the instances to the trained MIBP and selecting the one with maximal output value. The t is then obtained by averaging all the extracted instances. Finally, a sensitivity analysis of the trained MIBP is performed to obtain feature relevance/weighting information. We conducted experiments to measure the performance of the obtained t when used for classification purposes. The experimental results on the musk data set and a subset of the Corel image data set show that our method has better classification performance and is more computationally efficient than other well-established MIL methods.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".