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Projects / Programmes source: ARIS

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 (rules)
source: COBISS
Researchers (1)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  15295  PhD Marko Robnik Šikonja  Computer science and informatics  Head  2002 - 2003  417 
Organisations (1)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  1539  University of Ljubljana, Faculty of Computer and Information Science  Ljubljana  1627023  16,002 
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.
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