A thesis submitted to the Faculty of Graduate and Postdoctoral Studies of the University of Ottawa in partial fulfillment of the requirements for the degree of Doctor of Philosophy
Bibliographic record
Abstract
Abstract The Feature Selection problem involves discovering a subset of features, such that a classifier built only with this subset would have better predictive accuracy than a classifier built from the entire set of features. A large number of algorithms have already been proposed for the feature selection problem. Although significantly different with regards to 1) the search strategy they use to determine the right subset of features and 2) how each subset is evaluated, feature selection algorithms are usually classified in three general groups: Filters, Wrappers and Hybrid solutions. In this thesis, we propose a new hybrid system for the problem of feature selection in machine learning. The idea behind this new algorithm, FortalFS, is to extract and combine the best characteristics of filters and wrappers in one algorithm. FortalFS uses results from another feature selection system as a starting point in the search through subsets of features that are evaluated by a machine learning algorithm. With an efficient search heuristic, we can decrease the number of subsets of features to be evaluated by the learning algorithm, consequently decreasing computational effort and still be able to select an accurate subset. We have also designed a variant of the original algorithm in the attempt to work with feature weighting algorithm. In order to evaluate this new algorithm, a number of experiments were run and the results compared to well-known feature selection filter and wrapper algorithms, such as Focus, Relief, LVF, and others. Such experiments were run over a number of datasets from the UCI Repository. Results showed that FortalFS outperforms most of the algorithms significantly. However, it presents time-consuming performance similar to that of wrappers. Additional experiments using specially designed artificial datasets demonstrated that FortalFS is able to identify and remove both irrelevant, redundant and randomly
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.207 | 0.061 |
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".