Inductive logic programming for data mining in economics

• Type: Article in International Conference Proceedings Book
• Date: 2004

Authors

Alexessander Alves (Author) Other; Rui Camacho (Author) FEUP; Eugénio Oliveira (Author) FEUP

Conference proceedings International

Title: Proceedings of the 2nd International Workshop on Data Mining and Adaptive Modelling Methods for Economics and Management

Pages: 1-15

2nd International Workshop on Data Mining and Adaptive Modelling Methods for Economics and Management

Pisa, Italy, September 2004

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-010-VEE

Abstract (EN): This paper addresses the problem of data mining in Inductive Logic Programming (ILP) motivated by its application in the domain of economics. 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 new search stopping criterium inspired in the PAC learning framework. We have found these extensions essential to improve on results over machine learning and statistical-based algorithms used in the empirical evaluation study.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15