Title :
Discovery of functional relationships in multi-relational data using inductive logic programming
Author :
Alves, Alexessander ; Camacho, Rui ; Oliveira, Eugenio
Author_Institution :
LIACC, Porto, Portugal
Abstract :
ILP systems have been largely applied to data mining classification tasks with a considerable success. The use of ILP systems in regression tasks has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the application of ILP to discovery of functional relationships of numeric nature. This paper proposes improvements in numerical reasoning capabilities of ILP systems for dealing with regression tasks. It proposes the use of statistical-based techniques like model validation and model selection to improve noise handling and it introduces a search stopping criterium based on the PAC method to evaluate learning performance. We have found these extensions essential to improve on results over machine learning and statistical-based algorithms used in the empirical evaluation study.
Keywords :
data mining; inductive logic programming; inference mechanisms; learning (artificial intelligence); regression analysis; relational databases; PAC method; functional relationship discovery; inductive logic programming; machine learning; model selection; model validation; multirelational data; noise handling; numerical reasoning; regression tasks; search stopping; statistical-based techniques; Biochemistry; Cost function; Data mining; Impedance; Iterative algorithms; Logic programming; Machine learning; Machine learning algorithms; Proposals; Root mean square;
Conference_Titel :
Data Mining, 2004. ICDM '04. Fourth IEEE International Conference on
Print_ISBN :
0-7695-2142-8
DOI :
10.1109/ICDM.2004.10053