Use of non-monotonic utility in multi-attribute network selection
Bibliographic record
Abstract
Network convergence across different access technologies holds a promise of enabling ubiquitous service availability but faces several technical challenges. With anticipated proliferation of multimode IP devices, the optimal selection of a service delivery network among multiple IP based wireless access alternatives is one of the important issues that is actively studied and discussed in several standardization forums. Use of multi attribute decision making (MADM) algorithms has been proposed in the past for network selection decisions in a heterogeneous wireless network environment. A direct comparison of these algorithms is difficult as this would require the use of another MADM algorithm. A better approach instead is to ascertain the appropriateness of the algorithm to the problem space. This paper provides the basis for evaluating the appropriateness of MADM algorithms for network selection. It analyzes the use of MADM algorithms such as TOPSIS, ELECTRE and GRA for network selection and argues that GRA provides the best approach in scenarios where the utilities of some of the attributes are non-monotonic. The paper proposes a novel stepwise approach for GRA that uses multiple reference networks and explains its working with network selection scenarios.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".