Methods of attribute selection and principal component analysis: a comparative study
Bibliographic record
Abstract
Neoplasm is a major challenge for researchers because of its high complexity. Despite advances in diagnosis, studies point out that in addition to data analysis, methods to optimize and aid the decision-making process are necessary. In this sense, the dimensionality reduction of data has contributed significantly, helping in this process, due to the large number of genes (attributes) compared to the number of samples (classes). This work, therefore, aims to provide a comparative study between two methods of dimensionality reduction, applied to three databases in the field of gene expression: LungCancer-Michigan, LungCancer-Ontario and LungCancer-Harvard, all related to lung cancer. The methods applied were: Attribute Selection and Principal Component Analysis (PCA), both used as a pre-processing step in Data Mining. The classification algorithms chosen were Naive Bayes, SVM, J48, 1-NN, 3-NN, 5-NN and 7-NN. Weka was used as a software for analyses procedures. A series of experiments was performed to evaluate the accuracy and applicability of the algorithms for both methods. As a result, significant advances in the hit rate (accuracy) of the classifiers involving the methods were evidenced, using Cross-Validation as the assessment criterion. The Wrapper approach, from the Attribute Selection method, obtained the best results for the three analyzed databases. The Principal Component Analysis method, even presenting lower hit rate, could not be ruled out. The Naive Bayes, SVM and 1-NN algorithms presented the best performance within the databases. The attributes (genes) which presented the highest frequency in the databases were denoted. Therefore, from the chosen subsets, these can be submitted to specific analyzes in order to direct more precise diagnoses.
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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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".