Open AccessJournal Article
Transformation-based learning using multirelational aggregation
Mark-A. Krogel,Stefan Wrobel +1 more
103
TL;DR: In this paper, a multirelational learner that combines transformation-based approaches to ILP and relational aggregation is presented. But, even though not complex in structure, such business data often contain highly non-determinate components, making them difficult for ILP learners geared towards structurally complex tasks.
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Abstract: Given the very widespread use of multirelational databases, ILP systems are increasingly being used on data originating from such warehouses. Unfortunately, even though not complex in structure, such business data often contain highly non-determinate components, making them difficult for ILP learners geared towards structurally complex tasks. In this paper, we build on popular transformation-based approaches to ILP and describe how they can naturally be extended with relational aggregation. We experimentall y show that this results in a multirelational learner that outperforms a structurally-oriented ILP system both in speed and accuracy on this class of problems.
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Citations
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TL;DR: A unifying view on both systems in which 1BC works in language space, and 1BC2 works in individual space is presented, and a new, efficient recursive algorithm improving upon the original propositionalisation approach of 1BC is presented.
ILP turns 20
Stephen Muggleton,Luc De Raedt,David Poole,Ivan Bratko,Peter A. Flach,Katsumi Inoue,Ashwin Srinivasan +6 more
TL;DR: Using the analogy of a human biography, this paper recalls the development of the subject from its infancy through childhood and teenage years and shows how in each phase ILP has been characterised by an attempt to extend theory and implementations in tandem with theDevelopment of novel and challenging real-world applications.
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132
Inducing Multi-Level Association Rules from Multiple Relations
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Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics
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References
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TL;DR: In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.
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Stefan Kramer,Bernhard Pfahringer,Christopher Helma +2 more
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TL;DR: This work has devised a stochastic algorithm to automatically derive features from non-determinate background knowledge by conducting a top-down search for first-order clauses, where each clause represents a binary feature.
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A Framework for the Investigation of Aggregate Functions in Database Queries
Luca Cabibbo,Riccardo Torlone +1 more
- 10 Jan 1999
TL;DR: It is shown that numeric folding over a given vocabulary is sometimes not able to compute the whole class of uniform aggregate function over the same vocabulary, but this limitation can be partially remedied by the restructuring capabilities of a query language.
24
An extended transformation approach to inductive logic programming
Nada Lavrač,Peter A. Flach +1 more
TL;DR: This paper shows how this limitation of propositionalization methods such as LINUS can be overcome, by systematic first-order feature construction using a particular individual-centered feature bias, and shows how to improve upon exhaustive first- order feature construction by using a relevancy filter.