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Inductive logic programming - techniques and applications
Applications of inductive logic programming: learning rules for early diagnosis of rheumatic diseases finite element mesh design an overview of selected ILP applications.
Subgroup Discovery with CN2-SD
- N. Lavrac, B. Kavšek, Peter A. Flach, L. Todorovski
- Computer ScienceJ. Mach. Learn. Res.
- 1 December 2004
A subgroup discovery algorithm, CN2-SD, developed by modifying parts of the CN2 classification rule learner: its covering algorithm, search heuristic, probabilistic classification of instances, and evaluation measures, shows substantial reduction of the number of induced rules, increased rule coverage and rule significance, as well as slight improvements in terms of the area under ROC curve.
The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains
The demonstration that by applying the proposed method of cover truncation and analogical matching, called TRUNC, one may drastically decrease the complexity of the knowledge base without affecting its performance accuracy is demonstrated.
Rule Evaluation Measures: A Unifying View
This paper develops a unifying view on some of the existing measures for predictive and descriptive induction by means of contingency tables, and demonstrates that many rule evaluation measures developed for predictive knowledge discovery can be adapted to descriptive knowledge discovery tasks.
Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining
It is shown that various rule learning heuristics used in CSM, EPM and SD algorithms all aim at optimizing a trade off between rule coverage and precision.
APRIORI-SD: ADAPTING ASSOCIATION RULE LEARNING TO SUBGROUP DISCOVERY
An experimental comparison with rule learners CN2, RIPPER, and APRIorI-C on UCI data sets demonstrates that APRIORI-SD produces substantially smaller rulesets, where individual rules have higher coverage and significance.
Experiments with Noise Filtering in a Medical Domain
An Introduction to Inductive Logic Programming
This chapter introduces the basics of logic programming and relates logic programming terminology to database terminology, and defines the task of relational rule induction, the basic data mining task addressed by ILP systems, and presents some basic techniques for solving this task.
Selected techniques for data mining in medicine
- N. Lavrac
- Computer ScienceArtif. Intell. Medicine
- 1 May 1999
Foundations of Rule Learning
A feature-based view is introduced, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning.