Unsupervised wood species identification based on multiobjective optimal clustering and feature fusion
Bibliographic record
Abstract
Abstract This paper proposes an unsupervised wood species identification approach utilizing multiobjective optimization clustering and feature fusion. To address the limitations of single-band spectra in comprehensively capturing wood characteristics, we integrated preprocessed low-dimensional terahertz (THz) and hyperspectral data. Additionally, to tackle the issue of determining the optimal k-value in clustering, we developed an unsupervised wood clustering algorithm that employs multiobjective optimization and evolutionary algorithms. This algorithm utilized a prototype coding method for initialization, density peak clustering, and an improved firefly optimization algorithm for cross-variation to ensure population diversity. Furthermore, a selection operator was designed based on grid division and fast, non-dominated sorting. We evaluated the model's performance using a dataset consisting of hyperspectral and THz spectra from 400 samples representing ten wood species, comprising five coniferous and five broadleaf species. Experimental results demonstrated a 3.5% enhancement in clustering purity with fused data compared to individual datasets. Our proposed algorithm outperformed comparative methods such as DBSCAN, OPTICS, and peak density clustering, achieving a maximum clustering purity of 91.25% in both the internal and external clustering metrics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".