Proposing a Pareto-VIKOR Ranking Method for Enhancing Parallel Coordinates Visualization
Bibliographic record
Abstract
Data visualization is an essential step in data science to get better interpretation to analyse data. The parallel coordinates plot (PCP) is a well-known method to visualize high-dimensional (D > 3) data without dimension reduction. In large-scale datasest, PCP may fail because of many clutters and crossing lines in the plot. The order of coordinates is one of the parameters in PCP which can affect on the performance of this method. Finding the best order can be considered as a multi-criteria comparison task based on different metrics such as minimizing the number of crossing lines between adjacent coordinates and the maximizing the pairwise correlation coefcient values. In order to improve the visualization of data using PCP, this paper presents a multi-metric Pareto-VIKOR ranking (PVRPCP), a new method which determines the best order of coordinates based on optimizing two or more metrics. The method consists of evaluating all possible coordinates permutations based on evaluation metrics and applying non-dominated sorting algorithm (NDS) to obtain the Pareto-front ranks (PF). The solutions on each Pareto front are then ranked by VIKOR, a multi-criteria decision making measure. In order to evaluate the effectiveness of the the proposed method in data visualization, we also designed several multi-dimensional benchmarks to represent the effect of ordering in PCP. In addition to author-created benchmarks, several multi-objective function benchmarks and real-world datasets are utilized to evaluate the proposed method. The experimental results show that the PVRPCP offers improved PCP visualization compared to the original order in terms of both utilized 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".