Modified local discriminant bases and its applications in signal classification [biomedical signal examples]
Bibliographic record
Abstract
One of the major challenges in classification problems, based on the signal decomposition approach, is to identify the right basis function and its derivatives that can provide optimal features to distinguish the classes. With the vast amount of available libraries of orthonormal bases, it is hard to select an optimal set of basis functions for a specific dataset. To address this problem, pruning algorithms based on certain selection criteria, are needed. The local discriminant bases (LDB) algorithm is one such algorithm, which efficiently selects a set of significant basis functions from the library of orthonormal bases based on a certain defined dissimilarity measure. The selection of this dissimilarity measure is critical as they indirectly contribute to the performance accuracy of the LDB algorithm. In this paper, we study the impact of the dissimilarity measures on the performance of the LDB algorithm with two classification examples. Two biomedical signal databases used are: 1) vibroarthographic signals (VAG) - 89 signals with 51 normal and 38 abnormal; and 2) pathological speech signals - 100 signals with 50 normal and 50 pathological. Classification accuracies of 76.4% with the VAG database and 96% with the pathological speech database were obtained. This modified method of signal analysis using LDB has shown its powerfulness in analyzing non-stationary signals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".