Filters
cris logo
-
New search Filters
Select from list
Displayed from Show more
You have reached the maximum number of displayed search results.
All results displayed
No results are available for the search performed
Displayed from
You have reached the maximum number of displayed search results.
All results displayed
Loading results
Obtaining statistical data

Projects / Programmes source: ARIS

Cost sensitive intelligent data analysis

Edit
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 (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (1)
no. Code Name and surname Research area Role Period No. of publications
1.  15295  PhD Marko Robnik Šikonja  Computer science and informatics  Head  2002 - 2003  0 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  1539  University of Ljubljana, Faculty of Computer and Information Science  Ljubljana  1627023  0 
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.
Confirmation required
Views history
Favourite