A cognitive approach for attribute selection in internet dataset
Bibliographic record
Abstract
With the evolution of internet, there has been an unprecedented and unlimited growth in volume, velocity, veracity and variety of the data and the complexity of data attributes is on the rise. Further, in the domain of internet, data is not geo-centric any longer and multiple locations are contributing to the data acquisition technologies including but not limited to packet captures, data logs, routing and switching technologies and security detection and prevention systems. It can be stated that internet data is highly sparse with high dimensionality and an event can be represented by correlation of multiple attributes of a data set. Notwithstanding, analysis of such data set takes enormous human efforts and time. Machine learning has been found as promising candidate to extract information of interest from the data set but human cognitive analysis is required to feed to learning algorithms which is also called data preprocessing stage. This analysis requires cognitive domain knowledge, experiential learning and complexity analysis and therefore, traditional models fail in selecting proper attributes due to nonexistence of cognitive aspects. In this work, authors have proposed a fractal based cognitive model for artificial neural network to extract important attributes from two different internet data sets.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".