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Combating the Small Sample Class Imbalance Problem Using Feature Selection
IEEE Transactions on Knowledge and Data Engineering, 2010The class imbalance problem is encountered in real-world applications of machine learning and results in a classifier's suboptimal performance. Researchers have rigorously studied the resampling, algorithms, and feature selection approaches to this problem.
Mike Wasikowski, Xue-wen Chen
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An efficient discriminant-based solution for small sample size problem
Pattern Recognition, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Das, Koel, Nenadic, Zoran
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Inverse Problems in the Theory of Means for Small Samples
Measurement Techniques, 2014We formulate inverse problems for measurement of means obtained from small samples. The estimates investigated are chosen from the classical means: arithmetic, geometric, harmonic, quadratic and contraharmonic. We find formulas for estimating unknown quantities using known means from two and three measurements.
L. A. Mironovsky, V. A. Slaev
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Small sample size problem of fault diagnosis for process industry
IEEE ICCA 2010, 2010Fisher Discriminant analysis is one of the most common used fault diagnosis methods of process industry. But it is not satisfactory in practice. In recent years, kernel methods draw much attention as excellent ability for nonlinear problem. Unfortunately, more severe small sample size (3S) problem will be brought.
ChunMei Yu +3 more
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Generalized nonlinear discriminant analysis and its small sample size problems
Neurocomputing, 2011This paper develops a generalized nonlinear discriminant analysis (GNDA) method and deals with its small sample size (SSS) problems. GNDA is a nonlinear extension of linear discriminant analysis (LDA), while kernel Fisher discriminant analysis (KFDA) can be regarded as a special case of GNDA.
Li Zhang, Wei Da Zhou, Pei-Chann Chang
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Problems with Bootstrapping Pearson Correlations in Very Small Bivariate Samples
Psychometrika, 1982Efron's Monte Carlo bootstrap algorithm is shown to cause degeneracies in Pearson's r for sufficiently small samples. Two ways of preventing this problem when programming the bootstrap of r are considered.
Michael Dolker +2 more
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Statistical Problems With Small Sample Size
American Journal of Psychiatry, 1993LEE BAER, DAVID K. AHERN
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On Using Bayesian Methods to Address Small Sample Problems
Structural Equation Modeling: A Multidisciplinary Journal, 2016As Bayesian methods continue to grow in accessibility and popularity, more empirical studies are turning to Bayesian methods to model small sample data. Bayesian methods do not rely on asympotics, a property that can be a hindrance when employing frequentist methods in small sample contexts.
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A Gradient Linear Discriminant Analysis for Small Sample Sized Problem
Neural Processing Letters, 2007The purpose of conventional linear discriminant analysis (LDA) is to find an orientation which projects high dimensional feature vectors of different classes to a more manageable low dimensional space in the most discriminative way for classification. The LDA technique utilizes an eigenvalue decomposition (EVD) method to find such an orientation.
Sharma, Alok, Paliwal, Kuldip K
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Does size matter? Authorship attribution, small samples, big problem
Digital Scholarship in the Humanities, 2013The aim of this study is to find such a minimal size of text samples for authorship attribution that would provide stable results independent of random noise. A few controlled tests for different sample lengths, languages, and genres are discussed and compared. Depending on the corpus used, the minimal sample length varied from 2,500 words (Latin prose)
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