An Empirical Study on Structured Dichotomies in Music Genre Classification
Bibliographic record
Abstract
Ensemble learning approaches have been gaining popularity in the non-trivial task of multi-class classification. Some of these, including 1-against-1, 1-against-all, and dichotomy-based methods, are based on decomposing the class space of a multi-class task into a set of binary-class ones. In this work, we investigate whether they could help improve genre classification in music. In particular, we explore various dichotomy structures of binary classifiers in music data. In addition to the existing ones, we propose several strategies to build new binary tree structures. We base our approach on the observation that people find it easy to distinguish between certain classes and difficult between others. In our investigation, we use several base classifiers that are common in the literature and conduct series of empirical experiments on two benchmarking music datasets. We report the initial results of our investigation in this paper.
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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 | 0.000 | 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".