Leila Shila Shafti

Orcid: 0000-0001-8642-0804

According to our database1, Leila Shila Shafti authored at least 12 papers between 2003 and 2013.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2013
Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction.
J. Ambient Intell. Smart Environ., 2013

2012
Personal Ambient Intelligent Reminder for People with Cognitive Disabilities.
Proceedings of the Ambient Assisted Living and Home Care - 4th International Workshop, 2012

Evolutionary Feature Extraction to Infer Behavioral Patterns in Ambient Intelligence.
Proceedings of the Ambient Intelligence - Third International Joint Conference, 2012

2009
Evolutionary multi-feature construction for data reduction: A case study.
Appl. Soft Comput., 2009

Feature Construction and Feature Selection in Presence of Attribute Interactions.
Proceedings of the Hybrid Artificial Intelligence Systems, 4th International Conference, 2009

2008
Data Reduction by Genetic Algorithms and Non-Algebraic Feature Construction: A Case Study.
Proceedings of the 8th International Conference on Hybrid Intelligent Systems (HIS 2008), 2008

2007
MDL-based fitness for feature construction.
Proceedings of the Genetic and Evolutionary Computation Conference, 2007

Fitness Function Comparison for GA-Based Feature Construction.
Proceedings of the Current Topics in Artificial Intelligence, 2007

Reducing complex attribute interaction through non-algebraic feature construction.
Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, 2007

2005
Constructive induction and genetic algorithms for learning concepts with complex interaction.
Proceedings of the Genetic and Evolutionary Computation Conference, 2005

2004
Machine Learning by Multi-feature Extraction Using Genetic Algorithms.
Proceedings of the Advances in Artificial Intelligence, 2004

2003
Genetic Approach to Constructive Induction Based on Non-algebraic Feature Representation.
Proceedings of the Advances in Intelligent Data Analysis V, 2003


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