Acquiring knowledge with limited experience
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.
Document Type: Research Article
Affiliations: 1: Department of Industrial and Information Management, National Cheng Kung University, 1 University Road, Tainan 701, Taiwan 2: Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan, Email: [email protected]
Publication date: July 1, 2007