Determination of the Classification Success of KNN Algorithm Distance Metric Methods on Wheat Seeds Dataset
Bibliographic record
Abstract
Machine learning algorithms are widely used in product sorting processes in the food industry. The attributes of the products are used in the classification process. Attributes vary for each product. In this study, using the k nearest neighbor (KNN) algorithm, the classification of the wheat groups of Kama, Rosa and Canada was performed. The Seeds dataset provided in UCI (University of California, Irvine) machine learning open source data storage was used. There are 70 examples of each wheat class in the data set. In addition, the classification estimation success of distance metrics and the number of training data was measured. Each of the wheat samples was randomly selected and a soft X-ray technique was used to visualize the inner core structure of the wheat in the experimental environment with high quality. According to the training rates ranging from 50% to 90% of the data set, the classification success of the KNN algorithm was tested. In the KNN algorithm, the neighborhood values 1, 3 and 5 were selected to affect the classification success. The successes of the Euclidean, Chebyshev, Manhattan and Mahalanobis distance metric methods of the KNN algorithm were tested according to each k neighborhood value. According to the results obtained, with the Mahalanobis metric method, a classification success rate of 0.9924 accuracy was obtained according to the AUC (Area Under the Curve) success metric by using the neighborhood value of k = 3. In the literature, there is no study comparing the KNN algorithm, neighborhood values and distance vectors together on food data sets using varying training and test data. Therefore, it is thought that the study will make an important contribution to the literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".