Bibliographic record
Abstract
The Knowledge Discovery in Databases (KDD) comprises a number of stages, such as understanding its goals, acquiring, cleaning and integrating relevant data sources, modeling the obtained data sets by means of feature extraction and selection in order to prepare them as inputs to intelligent data analysis algorithms, and finally interpreting the results of those algorithms in practice.The success of KDD depends, among many other factors, on the ability to interact with the domain experts and the availability of effective data and information sharing.The domain knowledge is crucial for the design and tuning of practically all KDD stages.Privacy-preserving is crucial to run intelligent systems without violation of individual privacy by exposing potentially sensitive person-specific information.This special issue aims at elaborating on the state of the art in the areas of privacy-aware intelligent systems and the utilization of the domain knowledge in KDD.It is inspired by two workshops held at the 20th International Symposium on Methodologies for Intelligent Systems in Macau, China, 4-7 December 2012.The issue contains six carefully extended and thoroughly peer-reviewed articles, which were originally presented at the workshops.Their topics refer to data clustering and similarity, association rule induction and multi-label classification, as well as database engines, search systems and web services.Their results can contribute to a number of areas in our everyday lives, such as medical and healthcare systems, social and scientific networks, administrative services and so on.The first paper, by Jan Rauch, is titled "Formal Framework for Data Mining with Association Rules and Domain Knowledge -Overview of an Approach".The author outlines a methodology for data mining with association rules.The framework is based on a logical, appropriately enhanced calculus of rules.It allows to describe the whole knowledge discovery process, including formulation of analytical questions, application of the proposed analytical procedure and interpretation of its results.The domain knowledge is expressed using a formalized language aimed at deriving truly relevant rules.The second paper, by Nicolas Anciaux, Danae Boutara, Benjamin Nguyen and Michalis Vazirgiannis, is titled "Limiting Data Exposure in Multi-Label Classification Processes".The authors consider decision making processes based on multi-label classification models in the area of administrative services.They continue their previous research on minimizing data required from applicants as an input to online forms.They develop an experimental platform able to transform arbitrary multi-label data sets into collection rules and to measure the gain obtained in terms of minimizing data exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.011 |
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 teacher head, 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".