Abstract:
In this paper, a fuzzy pattern classification tuning approach is proposed, which is based on fusion concept. In this method, tuning parameters are learned in a training p...Show MoreMetadata
Abstract:
In this paper, a fuzzy pattern classification tuning approach is proposed, which is based on fusion concept. In this method, tuning parameters are learned in a training procedure, enabling system to be capable of managing individual classification task. Fuzzy c-means, as a specific instance of Tuning Reference, is employed as a tool to offer membership function which is used for making decisions and its membership function fuses (tunes) another membership function captured from fuzzy pattern classification and then final decisions are made upon fused one. Experiments are taken on five benchmark datasets, one of them shows an equal performance and the other four present better results than each single classifier.
Published in: 2008 11th International Conference on Information Fusion
Date of Conference: 30 June 2008 - 03 July 2008
Date Added to IEEE Xplore: 26 September 2008
ISBN Information:
Conference Location: Cologne, Germany
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- IEEE Keywords
- Index Terms
- Fuzzy Pattern ,
- Tuning Parameter ,
- Benchmark Datasets ,
- Membership Function ,
- Individual Tasks ,
- Fuzzy C-means ,
- Fuzzy Classification ,
- Training Set ,
- Center Of Mass ,
- Classification Performance ,
- Parameter Selection ,
- Cluster Centers ,
- Fuzzy Set ,
- Shaded Area ,
- Multi-agent Systems ,
- Proportional-integral-derivative ,
- Real Test ,
- Conflict Situations ,
- Wrong Classification ,
- Bigger Value ,
- Membership Values ,
- Fuzzy Rules ,
- Final Degree ,
- Real Class
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Fuzzy Pattern ,
- Tuning Parameter ,
- Benchmark Datasets ,
- Membership Function ,
- Individual Tasks ,
- Fuzzy C-means ,
- Fuzzy Classification ,
- Training Set ,
- Center Of Mass ,
- Classification Performance ,
- Parameter Selection ,
- Cluster Centers ,
- Fuzzy Set ,
- Shaded Area ,
- Multi-agent Systems ,
- Proportional-integral-derivative ,
- Real Test ,
- Conflict Situations ,
- Wrong Classification ,
- Bigger Value ,
- Membership Values ,
- Fuzzy Rules ,
- Final Degree ,
- Real Class
- Author Keywords