Pattern classification of time-series signals using Fisher kernels and support vector machines
Bibliographic record
Abstract
The objective of this study is to assess the performance and capability of a kernel-based machine learning method for time-series signal classification. Applying various stages of dimension transformation, training, testing and cross-validation, we attempt to perform a binary classification using the time-series signals from each category. This study has been applied to two domains: Financial and Biomedical. The financial domain study involves identifying the possibility of collapse or survival of a company trading in the stock market. For assessing the fate of each company, we collect its real stock market data, which is basically a financial time-series composed of weekly closing stock prices in a common time-series interval. This study has been applied to various economic sectors such as Pharmaceuticals and Biotechnology, Automobiles, Oil & Gas, Water Supply etc. The data has been collected using Thomson’s Datastream software. In the biomedical study we are dealing with knee signals collected using the Vibration arthrometry technique. This study involves using the severity of cartilage degeneration for assessing the possibility omachinf a subject getting affected by Osteoarthritis or undergoing knee replacement surgery at a later stage. This non-invasive diagnostic method can also prove be an alternative to various invasive procedures used for detecting osteoarthritis. For this analysis we have used the vibroarthro-signals for about 38 abnormal and 51 normal knee joint case studies. In both studies we apply Fisher Kernels incorporated with Gaussian Mixture Model (GMM) for dimension transformation into feature space created as a three-dimensional plot for visualization. The transformed data is then trained and tested using support vector machines for performing binary classification. From our experiments we observe that our method fits really well for both the studies with the classification error rate between 10% to 15%.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".