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Inductive logic programming - techniques and applications
We use attribute-value learners in an ILP framework to learn relations from noisy examples. Expand
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Subgroup Discovery with CN2-SD
This paper presents a subgroup discovery algorithm, CN2-SD, developed by modifying parts of the classification rule learner: its covering algorithm, search heuristic, probabilistic classification of instances, and evaluation measures. Expand
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The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains
AQ15 is a multi-purpose inductive learning system that uses logic-based, user-oriented knowledge representation, is able to incrementally learn disjunctive concepts from noisy or overlapping examples, and can perform constructive induction (i.e., can generate new attributes in the process of learning). Expand
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Rule Evaluation Measures: A Unifying View
This paper provides a systematic analysis of rule evaluation measures used in machine learning and knowledge discovery. Expand
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Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining
This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. Expand
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APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery
This paper presents a sub group discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup discovery, using a novel post-processing mechanism, a new quality measure for induced rules (weighted relative accuracy) and using probabilistic classification of instances. Expand
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Experiments with Noise Filtering in a Medical Domain
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Relational Data Mining
Data mining, the central activity in the process of knowledge discovery in databas es, is concerned with finding patterns in data . This chapter introduces and illustrates the most common types ofExpand
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An Introduction to Inductive Logic Programming
Inductive logic programming (ILP) is concerned with the development of techniques and tools for relational data mining. Expand
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The AQ15 Inductive Learning System: An Overview and Experiments
This research was supported in part by the National Science Foundation under Grant No. DCR 84-06801, the Office of Naval Research under Grant No. N00014-82-K-0186, the Defense Advanced ResearchExpand
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