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Adaptive local hyperplane algorithm for learning small medical data sets
Author(s) -
Yang Tao,
Kecman Vojislav
Publication year - 2009
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2009.00494.x
Subject(s) - computer science , hyperplane , benchmarking , classifier (uml) , artificial intelligence , machine learning , data set , algorithm , data mining , mathematics , geometry , marketing , business
It is not unique that only a few samples from medical studies are available for knowledge discovery. Hence, a suitable classifier for the small data set learning problem is very interesting in medical work. In this paper, we experiment with the adaptive local hyperplane algorithm on small medical data sets. The experimental results on two cancer data sets demonstrate that the proposed classifier outperforms, on average, all the other four benchmarking classifiers for learning small data sets.