Data flow coherence criteria in ILP tools

Author

Muresan, Smaranda ; Muresan, Tudor ; Potolea, Rodica

Author_Institution

Dept. of Comput. Sci., Columbia Univ., New York, NY, USA

fYear

2001

fDate

7-9 Nov 2001

Firstpage

179

Lastpage

186

Abstract

In this paper we present a new method that uses data flow coherence criteria in definite logic program generation. We outline three main advantages of these criteria supported by our results: (i) drastically pruning the search space (around 90%), (ii) reducing the set of positive examples and reducing or even removing the need for the set of negative examples, and (iii) allowing the induction of predicates that are difficult or even impossible to generate by other methods. Besides these criteria, the approach takes into consideration the program termination condition for recursive predicates. The paper outlines some theoretical issues and implementation aspects of our system for automatic logic program induction

Keywords

data flow computing; data mining; inductive logic programming; learning (artificial intelligence); search problems; ILP tools; automatic logic program induction; data flow coherence criteria; definite logic program generation; inductive logic programming; program termination condition; recursive predicates; search space; Automatic logic units; Benchmark testing; Computer science; Induction generators; Lattices; Logic programming; Machine learning; Machine learning algorithms; Magnetic heads; Natural language processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, Proceedings of the 13th International Conference on

Conference_Location

Dallas, TX

Print_ISBN

0-7695-1417-0

Type

conf

DOI

10.1109/ICTAI.2001.974463

Filename

974463