Decision Tree Learning Based Feature Selection and Evaluation for Image Classification
Bibliographic record
Abstract
Abstract: The problem statement focuses on feature evaluation and selection for image classification using decision tree learning. The objective is to identify the most significant features in an image dataset and train a decision tree classifier using these selected features. The accuracy of an image classifier heavily relies on the quality and relevance of the features used to represent the images. Hence, it is crucial to identify the most important features and eliminate the irrelevant ones to enhance the classifier's accuracy. To implement this approach, we can utilize scikit-learn, a popular machine learning library in Python. The solution must involve training a decision tree classifier on the dataset and extracting feature importances, selecting the top features using modules from sklearn like “SelectFromModel”, and also performing hyperparameter tuning using “GridSearchCV” and training a new decision tree classifier on the selected features with the best hyperparameters. Decision trees are a popular machine learning algorithm that uses a tree-like model of decisions and their possible consequences. By training a decision tree classifier on an image dataset and extracting feature importances, it is possible to identify the most important features and select them for use in a new decision tree classifier that can improve classification accuracy. It is important to note that decision tree learning is a versatile machine learning algorithm that can handle both binary and multiclass classification problems. Additionally, it is advantageous for feature evaluation and selection in image classification tasks. By identifying the most relevant features, this approach can enhance the accuracy of the classifier and reduce computational complexity, making it suitable for large datasets. By following this outlined approach, you can create a project that addresses feature evaluation, selection, and classification accuracy improvement using decision tree learning in the context of image classification
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".