A new method for learning decision trees from rules and its illustration for online identity application fraud detection
Bibliographic record
Abstract
A decision tree is a graph or model for representing all the alternatives in a decision making process. Most of the methods that generate decision trees for a specific problem use examples of data instances in the decision tree generation process. We propose a new method called "RBDT-1"- rule based decision tree -for learning a decision tree from a set of decision rules that cover the data instances. RBDT-l method uses a set of declarative rules as an input for generating a decision tree. The method's goal is to create on-demand a short and accurate decision tree from a stable or dynamically changing set of rules. The rules used by RBDT-1 could be generated either by an expert or induced directly from a rule induction method or indirectly by extracting them from a decision tree. We conduct a comparative study of RBDT-1 with four existing decision tree methods based on different problems. The outcome of the study shows that in terms of tree complexity (number of nodes and leaves in the decision tree) RBDT-1 compares favorably to AQDT-1 and AQDT-2 which are methods that create decision trees from rules. RBDT-1 compares favorably also to ID3 while is as effective as C4.5 where both (ID3 and C4.5) are famous methods that generate decision trees from data examples. Experiments show that the classification accuracies of the different decision trees produced by the different methods under comparison are equal. To illustrate how RBDT-1 can successfully be applied to an existing real life problem that could benefit from the method, we choose identity application fraud detection. We designed a new unsupervised framework to detect fraudulent applications for identity certificates by extracting identity patterns from the web, and crossing these patterns with information contained in the application forms in order to detect inconsistencies or anomalies. The outcome of this process is submitted to a decision tree classifier generated using RBDT-1 on the fly from a rule base which is derived from heuristics and expert knowledge, and updated as more information are obtained on fraudulent behavior. We evaluate the proposed framework by collecting real identity information online and generating synthetic fraud cases, achieving encouraging performance results.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".