|
| << previous | next >> |
Journal description
All volumes of the corresponding journal
All issues of the corresponding volume
All articles of the corresponding issues
Details for article 12 of 17 found articles
| Title: |
Knowledge discovery by means of inductive methods in wastewater treatment plant data |
| Author: |
Joaquim Comas Saso Dzeroski Karina Gibert Ignasi R.-Roda Miquel Sà nchez-Marrè |
| Appeared in: |
AI communications |
| Paging: | Volume 14 (2001) nr. 1 pages 45-62 |
| Year: |
2001-04-01 |
| Contents: |
Artificial intelligence techniques, including machine learning methods, and statistical techniques have shown promising results as decision support tools, because of their capabilities of knowledge discovery, heuristic reasoning and working with uncertain and qualitative information. Wastewater treatment plants are complex environmental processes that are difficult to manage and control. This paper discusses the qualitative and quantitative performance of several machine learning and statistical methods to discover knowledge patterns in data. The methods are tested and compared on a wastewater treatment data set. The methods used are: induction of decision trees, two different techniques of rule induction and two memory-based learning methods: instance-based learning and case-based learning. All the knowledge patterns discovered by the different methods are compared in terms of predictive accuracy, the number of attributes and examples used, and the meaningful-ness to domain experts. |
| Publisher: |
IOS Press |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 12 of 17 found articles
| << previous | next >> |