- author = "Sergejs Provorovs and Arkady Borisov",
- title = "Use of Linear Genetic Programming and Artificial Neural Network Methods to Solve Classification Task",
- journal = "Journal Riga Technical University",
- year = "2011",
- volume = "45",
- pages = "133--138",
- publisher = "Versita, Warsaw",
- keywords = "genetic algorithms, genetic programming, linear genetic programming, artificial neural networks, classification task, cross-validation",
- ISSN = "1407-7493",
-
URL = "
http://versita.metapress.com/content/v747725r3q041610/fulltext.pdf",
-
DOI = "
doi:10.2478/v10143-011-0055-9",
- size = "6 pages",
-
abstract = "This paper presents a comparative analysis of linear
genetic programming and artificial neural network
methods to solve classification tasks. Usually
classification tasks have data sets containing a large
number of attributes and records, and more than two
classes that will be processed using, for example,
created classification rules. As a result, by using
classical method to classify a large number of records,
a high classification error value will be obtained. The
artificial neural networks are often used to solve
classification task, mostly obtaining good results. The
linear genetic programming is a new direction of
evolution algorithms that is not widely researched and
its application areas are not well defined. However,
some advantages of linear genetic programming are based
on genetic operators whose structure does not require
complicated calculations.
During this work approximately 400 experiments were conducted with linear genetic programming and artificial neural network methods, using various data sets with different quantity of records, attributes and classes.
Based on the results received, conclusions on possibilities of using the methods of linear genetic programming and artificial neural networks in classification problems were drawn, and suggestions for improving their performance were proposed.",
- notes = "UCI: Iris, Glass, Heart, Zoo, Abalone",
- bibdate = "2012-03-07",
- bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/riga/riga45.html#ProvorovsB11",
