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Probability-possibility transformation for small sample size data
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010Data insufficiency and information incompleteness often exist in engineering practice, resulting in the presence of epistemic uncertainty. Possibility theory is effective in dealing with epistemic uncertainty and has been applied in various domains. In possibility theory, possibility distribution is an essential concept which has to be derived from ...
Yanhua Hou, Bo Yang
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Small sample size effects in the use of editing techniques
Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems, 2003Editing is recognized as a useful, convenient and even mandatory preprocessing to be carried out prior to using nearest-neighbor (NN) classification techniques. The performance of editing has only been shown for large sets of data but, in practice, one can seldom afford such large sets; either because of the cost of data collection and/or because of ...
Francesc J. Ferri, Enrique Vidal 0001
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Estimation of individual reference intervals in small sample sizes
International Journal of Hygiene and Environmental Health, 2007In occupational health studies, the study groups most often comprise healthy subjects performing their work. Sampling is often planned in the most practical way, e.g., sampling of blood in the morning at the work site just after the work starts. Optimal use of reference intervals requires that the population, on which the reference interval is based ...
Hansen, Ase Marie +3 more
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Multiple task transfer learning with small sample sizes
Knowledge and Information Systems, 2015Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data with small numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from ...
Budhaditya Saha +3 more
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Feature Extractions for Small Sample Size Classification Problem
IEEE Transactions on Geoscience and Remote Sensing, 2007Much research has shown that the definitions of within-class and between-class scatter matrices and regularization technique are the key components to design a feature extraction for small sample size problems. In this paper, we illustrate the importance of another key component, eigenvalue decomposition method, and a new regularization technique was ...
Bor-Chen Kuo, Kuang-Yu Chang
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NORMALITY TESTS FOR SMALL SAMPLE SIZES
Quality Engineering, 1994Normality of data samples must be established to accept the validity of process capability analysis and other statistical tests. The advantages and disadvantages of a number of normality tests are discussed; normal probability plots, skewness and kurtos..
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Hypothesis Testing and Small Sample Sizes
2001One of the biggest breakthroughs during the last 40 years has been the derivation of inferential methods that perform well when sample sizes are small. Indeed, some practical problems that seemed insurmountable only a few years ago have been solved. But to appreciate this remarkable achievement, we must first describe the shortcomings of conventional ...
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