- author = "Alex A. Freitas",
- title = "A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction",
- booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference",
- editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo",
- year = "1997",
- month = "13-16 " # jul,
- keywords = "genetic algorithms, genetic programming, SQL",
- pages = "96--101",
- address = "Stanford University, CA, USA",
- publisher_address = "San Francisco, CA, USA",
- publisher = "Morgan Kaufmann",
-
URL = "
http://citeseer.nj.nec.com/43454.html",
-
URL = "http://kar.kent.ac.uk/21483/",
-
URL = "
http://kar.kent.ac.uk/21483/2/A_Genetic_Programming_Framework_for_Two_Data_Mining_Tasks_Classification_and_Generalized_Rule_Induction.pdf",
- size = "6 pages",
- abstract = "This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalised rule induction. The framework emphasises the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization. The paper also proposes some genetic operators tailored for the two above data mining tasks.",
-
notes = "GP-97
Lazy learning, separation of query tree encodes Tuple-Set Descriptor (SQL), from goal attribute. Goal subject to three types of mutation",
