Adaptive Hyper-Box Granulation With Justifiable Granularity for Feature Selection
Bibliographic record
Abstract
Clustering as a fundamental technique in data mining and machine learning, aims to partition data into meaningful groups based on the inherent relationships among data. However, traditional clustering algorithms typically assume convex hyperspherical geometry of data, where the clusters have clearly defined boundaries and do not overlap. In contrast, real-world data often exhibits complex and non-convex geometries, which makes these assumptions ineffective and lead to inaccurate clustering results that fail to capture the intrinsic structure. To address this challenge, the paper proposes a novel granular clustering based on an enhanced granularity representation, which further refines the principle of justifiable granularity. By introducing a more precise and flexible hyper-box granulation mechanism, the method dynamically adapts to the topology of data, thereby improving clustering accuracy. By defining the degree of aggregation and discreteness between data points, the importance of attributes in the feature space is quantified, leading to the design of a novel hyper-box feature selection (HBFS) algorithm. This algorithm integrates the granular clustering principle to optimize the feature selection process, reducing the impact of redundant features and noise, thus improving clustering efficiency and interpretability. To validate the superiority and effectiveness of the proposed method, extensive experiments were conducted on fifteen publicly available datasets, comparing the performance of HBFS algorithm with classical and state-of-art feature selection methods. The results and the statistical significance tests show that HBFS significantly outperforms existing feature selection methods across various evaluation 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".