A Self-Organizing Map Artificial Neural Network to Improve the K-Means Algorithm on the Classification of Different Cancers
Bibliographic record
Abstract
In this paper, a method is presented in order to conduct data classification through the use of a Self-Organizing Map (SOM) Artificial Neural Network (ANN). At first, the performance of the K-means algorithm in relation to the classification of the data is analyzed completely. Then, the disadvantages and losses of this method are presented. By introducing the SOM ANN algorithm, the performance of the K-means algorithm with respect to data classification is modified. In fact, in this paper, the clustering performance of K-means is modified by the SOM ANN algorithm. For this aim, both algorithms are analyzed mathematically. Then, the problem results are compared in a simulation in MATLAB. The mentioned data are derived from 1000 patients with four different types of cancer. Each patient has two different symptoms. Four different cancers are shown as different clusters in here. The cancers are blood, intestine, salivary gland, and lung carcinoids. Symptoms are oxygen capacity of the lungs and red blood cell surface. The mentioned data were recorded by using a data acquisition system. The ANN network in this paper is based on the performance of an unsupervised learning problem.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".