Discretization and Fuzzification of Numerical Attributes in Attribute-Based Learning
Bibliographic record
Abstract
Machine learning (ML) algorithms have been capable of processing symbolic, categorial data only. Real-world problems, particularly in medicine, comprise not only symbolic, but also numerical attributes. There are several approaches to discretize (categorize) numerical attributes. This article describes two newer algorithms for such a discretization. The first one has been designed and implemented in KEX (Knowledge Explorer) as its preprocessing procedure. The other discretization procedure was designed for the CN4 algorithm, a large extension of the well-known CN2. The discretization procedure in CN4 works on-line, i.e., it dynamically (within the induction) discretizes numerical attributes. A large drawback of these discretization procedures, either off-line or on-line, is that they generate sharp bounds between intervals. One way how to eliminate an impurity around the interval borders is to fuzzify them. Here we introduce the newest empirical procedures for fuzzification, both off-line (within KEX) and on-line (CN4). This chapter first surveys the methodology of empirical machine learning (Section 1), then attribute-based rile-inducing learning from examples (Section 2). Section 3 briefly introduces the KEX algorithm and Section 4 surveys CN4. The last Section focuses on discretization and fuzzification procedures, includes empirical results that compare performance of KEX, CN4, and other well-known machine learning algorithms as for discretization and fuzzification, and concludes with analysis.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".