BOOTSTRAPPING AND WEIGHTED INFORMATION GAIN IN SUPPORT VECTOR MACHINE FOR CUSTOMER LOYALTY PREDICTION
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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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".