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ILP
2004
Springer
15 years 5 months ago
Macro-Operators Revisited in Inductive Logic Programming
For the last ten years a lot of work has been devoted to propositionalization techniques in relational learning. These techniques change the representation of relational problems t...
Érick Alphonse
IEEEICCI
2002
IEEE
15 years 4 months ago
Quasi-Morphism and Comprehensibility of Rules in Inductive Learning
We present a model of creating a hierarchical set of rules that encode generalizations and exceptions derived from induction learning. The rules use the input features directly an...
Wiphada Wettayaprasit, Chidchanok Lursinsap, Chee-...
DATAMINE
2008
143views more  DATAMINE 2008»
14 years 12 months ago
Automatically countering imbalance and its empirical relationship to cost
Learning from imbalanced datasets presents a convoluted problem both from the modeling and cost standpoints. In particular, when a class is of great interest but occurs relatively...
Nitesh V. Chawla, David A. Cieslak, Lawrence O. Ha...
ICML
1999
IEEE
16 years 18 days ago
Making Better Use of Global Discretization
Before applying learning algorithms to datasets, practitioners often globally discretize any numeric attributes. If the algorithm cannot handle numeric attributes directly, prior ...
Eibe Frank, Ian H. Witten
FUIN
2002
132views more  FUIN 2002»
14 years 11 months ago
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
The article describes a method combining two widely-used empirical approaches to learning from examples: rule induction and instance-based learning. In our algorithm (RIONA) decisi...
Grzegorz Góra, Arkadiusz Wojna