The Role of Ensemble Learning in Stock Market Classification Model Accuracy Enhancement Based on Naive Bayes Classifiers
Bibliographic record
Abstract
Over the last years, methods of hybrid and ensemble have attracted the attention of the data mining community. Moreover, in the computational intelligence area such as machine learning, constructing and adaptive hybrid models have become essential to achieve good performance. However, the accuracy of stock market classification models is still low, and this has negatively affected the stock market indicators. Furthermore, there are many factors that have a direct effect on the classification models’ accuracies which were not addressed by previous research such as the automatic labelling technique which results in low classification accuracy due to the absence of specific lexicon, and the suitability of the classifiers to the data features and domain. In this research, a proposed model is designed to enhance the classification accuracy by the incorporation of stock market domain expert labelling technique and the construction of an ensemble Naïve Bayes classifiers to classify the stock market sentiments. The methodology for this research consists of five phases. The first phase is data collection, and the second phase is labelling, in which polarity of data is specified and negative, positive or neutral values are assigned. The third phase involves data pre-processing. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by Ensemble Naïve Bayes classifiers, and the final is the performance and evaluation. The classification method has produced a significant result; it has achieved accuracy of more than 89%.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".