Inconsistency detection in cancer data classification using explainable-AI
Bibliographic record
Abstract
A bstract This paper presents a novel approach to improving text-based cancer data classification by integrating BERTopic clustering with Support Vector Machine (SVM) classifiers, combined with the Explainable Inconsistency Algorithm (EIA). The proposed method leverages advanced preprocessing techniques, including Node2Vec embeddings, to enhance both clustering and classification performance. Through the introduction of EIA, we automatically identify and eliminate outliers and discordant data points, thus improving classification accuracy and providing valuable insights into underlying data relation-ships. A key innovation in this work is the use of recommender systems for mapping clusters to labels, which improves label assignment through collaborative filtering techniques. Our experimental results show a significant increase in both accuracy and F1-score after addressing data inconsistencies, with improvements validated through statistical tests, including t-tests. This paper contributes a robust, explainable, and scalable framework for cancer data analysis, offering potential applications in other domains requiring high-precision text classification. Future work will focus on extending the EIA to other biomedical datasets, optimizing hyperparameters, and deploying the framework in real-time clinical decision-support systems.
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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 | 0.001 | 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 | 0.002 | 0.005 |
| 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 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".