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Projects / Programmes
Cost sensitive intelligent data analysis
Research activity
| Code | Science | Field | Subfield |
|---|---|---|---|
| 2.07.07 | Engineering sciences and technologies | Computer science and informatics | Intelligent systems - software |
| Code | Science | Field |
|---|---|---|
| P176 | Natural sciences and mathematics | Artificial intelligence |
Keywords
machine learning, intelligent data analysis, inductive learning, cost-sensitive learning, attribute estimation
Evaluation
(metodology)
Citations
Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
Organisations (1) , Researchers (1)
1539 University of Ljubljana, Faculty of Computer and Information Science
| no. | Code | Name and surname | Research area | Role | Period | No. of publicationsNo. of publications |
|---|---|---|---|---|---|---|
| 1. | 15295 | Computer science and informatics | Head | 2002 - 2003 | 473 |
Abstract
Many important practical problems from intelligent data analysis assume that all outcomes are not equally important i.e., that they have different costs assigned. The inductive learning with this assumption is currently a hot research topic.
Algorithms Relief are among the best algorithms for attribute estrimation. They were successfully used in many machine learning tasks. So far these algorithms are not adapted for cost-sensitive classifiication and such an adaptation together with its implementation in a system for intelligent data analysis could significantly improve the success of solving the cost sensitive problems.
With this project we will analyse various extensions and adaptations of ReliefF algorithm for cost sensitive intelligent data analysis. Theoretical derivations and analyses will be implemented in the system for intelligent data analysis which we will adapt for cost sensitive problems. We will test the solutions on several artificial and real world problems.