BOOTSTRAPPING AND WEIGHTED INFORMATION GAIN IN SUPPORT VECTOR MACHINE FOR CUSTOMER LOYALTY PREDICTION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Prediction customer loyalty is an important business strategy for the modern telecommunications industry in the global competition. Support Vector Machine (SVM) is a classification algorithm that widely used to predict the customer loyalty. SVM in predicting customer loyalty has a weakness that affects the accuracy in the prediction. The problem is the difficulty of kernel function selection and determination of the parameter value. Large datasets may contain the imbalance class. In this study, bootstrapping method is used to overcome the imbalance class. In addition, datasets also contain some features that are not relevant to the prediction. In this study, we propose to use Forward Selection (FS) and Weighted Information Gain (WIG). FS eliminates the most irrelevant features and the computation time is relatively short compared to backward elimination and stepwise selection. WIG is used to weight the each attribute. In order to handle the selection of SVM parameters, we use a grid search method. Grid search method find the best parameter value by providing parameter value range. The experimental results from some combination of parameters can be concluded that the prediction of customer loyalty by using samples bootstrapping, FS-WIG and grid search is more accurate than the individual SVM.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it