Title :
SOM Classification Method based on Transduction Scheme
Author :
Tong, Bin ; Qin, Zhi-guang ; Ma, Xin-xin ; Wang, Yong ; Jia, Wei-feng
Author_Institution :
Sch. of Comput. Sci. & Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu
Abstract :
Transductive confidence machines (TCMs) when used in classification problems can provide us with reliability for every classification. Many machine learning algorithms, such as KNN algorithm, etc., have been incorporated with TCM, while there´s no SOM classification method based on TCM. Considering properties of SOM map unit, this paper first designs a novel nonconformity measurement and TCM-SOM classification method; and then its classification accuracy that is much more better than that of SOM and is close or even higher than that of TCM-KNN is also proved by UCI machine learning datasets.
Keywords :
learning (artificial intelligence); pattern classification; self-organising feature maps; KNN algorithm; SOM classification method; UCI machine learning dataset; machine learning algorithm; transductive confidence machine; Algorithm design and analysis; Computer science; Design methodology; Electronic mail; Error analysis; Machine learning; Machine learning algorithms; Reliability engineering; Support vector machine classification; Support vector machines; Machine learning; TCM-KNN; TCM-SOM; classification;
Conference_Titel :
Apperceiving Computing and Intelligence Analysis, 2008. ICACIA 2008. International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-3427-5
Electronic_ISBN :
978-1-4244-3426-8
DOI :
10.1109/ICACIA.2008.4769960
