Discovering Business Problems Using Problem Hypotheses: A Goal-Oriented and Machine Learning-Based Approach
Bibliographic record
Abstract
Discovering business problems hindering business goals and a deep understanding of those problems are often more important than finding solutions. However, business organizations face difficulties finding business problems hidden in Big Data using machine learning. The specific difficulties might include a lack of methods in exploring potential problems, figuring out necessary data features associated with potential problems, and validating potential problems. This paper presents the Metis+ framework that supports the discovery of business problems using problem hypotheses. Metis+ consists of essential modeling concepts, semantic reasoning methods, and processes for ensuring that potential business problems are analyzed in the context of business goals, hypothesized problems are systematically and explicitly mapped to relevant data features in a data set, and supervised machine learning models are built to get insights into problem hypotheses. Semantic reasoning methods are then utilized to validate or invalidate hypothesized problems. The Metis+ framework is illustrated with a loan case study in the PKDD'99 Financial Data Set. The experiment results show that Metis+ can help explore and validate problem hypotheses, leading to identifying the most significant 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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".