Learning a multi-criteria classification method using machine learning & metaheuristics techniques
Bibliographic record
Abstract
Data classification is a widely used approach in the area of data mining. Methodologies for addressing data classification have been developed in a variety of research disciplines, including artificial intelligence (AI) and Multi-Criteria Decision Aid (MCDA). The objective of this thesis is to develop a new framework for learning the MCDA method PROAFTN. The limitations of PROAFTN are largely due to the set of parameters required to be obtained to perform the classification procedure. That is, to apply PROAFTN, the values of several parameters need to be determined prior to classification, such as boundaries of intervals and weights. In an MCDA context, these parameters are usually dependent on the judgment of the decision maker (DM). This approach has shortcomings, such as being time consuming and dependent on the availability of a qualified DM. To overcome these limitations and to obtain the best parameters from data, an automatic approach is proposed in this work . This thesis introduces new methodologies based on using machine learning and metaheuristic techniques for establishing PROAFTN parameters from data during the training process. The goal is to obtain from training data the best PROAFTN parameters that achieve the highest classification accuracy. To achieve this, different learning methodologies are proposed in this thesis. Firstly, discretization techniques and an inductive approach are introduced to obtain the required parameters for PROAFTN. Secondly, a different approach based on metaheuristic/hybrid-metaheuristic algorithms is used to develop PROAFTN parameters. The use of metaheuristics to learn PROAFTN begins with the formulation of the optimization problem. Then, population-based methods, namely Particle Swarm Optimization (PSO) and Differential Evolution (DE), and the single-point search method Reduced Variable Neighborhood Search (RUNS) are utilized to obtain the best PROAFTN parameters that can be applied on unseen datasets. To test the performance of the proposed learning approaches, their effectiveness in classification is evaluated on several public-domain datasets and compared to a number of well-known machine learning classifiers. Advanced statistical tests such as the Friedman and Nemenyi tests are used for more meaningful comparisons. The general comparative study, including computational results, demonstrates that the proposed approaches are very competitive with and outrank widely used classification algorithms.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".