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Author(s) -
Li DerChiang,
Yeh ChunWu,
Tsai TungI,
Fang YaoHwei,
Hu Susan C.
Publication year - 2007
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.1468-0394.2007.00427.x
Subject(s) - computer science , machine learning , artificial intelligence , backpropagation , artificial neural network , support vector machine , set (abstract data type) , sample (material) , online machine learning , decision tree , chemistry , chromatography , programming language
From computational learning theory, sample size in machine learning problems indeed affects the learning performance. Since only few samples can be obtained in the early stages of a system and fewer exemplars usually lead to a low learning accuracy, this research compares different machine learning methods through their classification accuracies to improve small‐data‐set learning. Techniques used in this paper include the mega‐trend diffusion technique, a backpropagation neural network, a support vector machine, and decision trees to explore the machine learning issue with two real medical data sets concerning cancer. The result of the experiment shows that the mega‐trend diffusion technique and backpropagation approaches are effective methods of small‐data‐set learning.