Abstract
The effects of sample size on feature selection and error estimation for several types of classifiers are discussed. The focus is on the two-class problem. Classifier design in the context of small design sample size is explored. The estimation of error rates under small test sample size is given. Sample size effects in feature selection are discussed. Recommendations for the choice of learning and test sample sizes are given. In addition to surveying prior work in this area, an emphasis is placed on giving practical advice to designers and users of statistical pattern recognition systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Keywords
Sample size determinationComputer scienceClassifier (UML)Feature selectionArtificial intelligenceSample (material)Pattern recognition (psychology)Machine learningFeature (linguistics)Statistical hypothesis testingStatisticsData miningMathematics
Affiliated Institutions
Related Publications
Recognition of handwritten digits by combining independent learning vector quantizations
Classifiers derived by learning vector quantization (LVQ) have well-defined decision regions that can be combined to construct a more accurate classifier. A given point may be i...
Decision combination in multiple classifier systems
A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of ar...
Good features to track
No feature-based vision system can work unless good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, select...
Experiments on neural net recognition of spoken and written text
The problems are discussed of the recognition of handprinted and spoken digits and the handprinted and spoken English alphabet. Four such experiments were conducted and the resu...
Nearest neighbor pattern classification
The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent o...
Publication Info
- Year
- 1991
- Type
- article
- Volume
- 13
- Issue
- 3
- Pages
- 252-264
- Citations
- 1364
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
1364
OpenAlex
Cite This
Šarūnas Raudys,
Anil K. Jain
(1991).
Small sample size effects in statistical pattern recognition: recommendations for practitioners.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, 13
(3)
, 252-264.
https://doi.org/10.1109/34.75512
Identifiers
- DOI
- 10.1109/34.75512