An Improved Virtual Sample Generation Method Based on Quadrat Density Method and Quantile Regression for Small Sample Size Problem

2021 ◽  
Author(s):  
Qun-Xiong Zhu ◽  
Meiyu Zhu ◽  
Yuan Xu ◽  
Yan-Lin He
Keyword(s):  
Quantile Regression ◽  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Small Sample Size Problem ◽  
Virtual Sample ◽  
Density Method ◽  
Size Problem

scholarly journals A Novel Virtual Sample Generation Method to Overcome the Small Sample Size Problem in Computer Aided Medical Diagnosing

Algorithms ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 160 ◽  
Author(s):  
Mohammad Wedyan ◽  
Alessandro Crippa ◽  
Adel Al-Jumaily
Keyword(s):  
Sample Size ◽  
Small Sample Size ◽  
Classification Performance ◽  
Small Sample ◽  
Classification Model ◽  
Clinical Samples ◽  
Small Sample Size Problem ◽  
Virtual Sample ◽  
Size Problem ◽  
Virtual Samples

Deep neural networks are successful learning tools for building nonlinear models. However, a robust deep learning-based classification model needs a large dataset. Indeed, these models are often unstable when they use small datasets. To solve this issue, which is particularly critical in light of the possible clinical applications of these predictive models, researchers have developed approaches such as virtual sample generation. Virtual sample generation significantly improves learning and classification performance when working with small samples. The main objective of this study is to evaluate the ability of the proposed virtual sample generation to overcome the small sample size problem, which is a feature of the automated detection of a neurodevelopmental disorder, namely autism spectrum disorder. Results show that our method enhances diagnostic accuracy from 84%–95% using virtual samples generated on the basis of five actual clinical samples. The present findings show the feasibility of using the proposed technique to improve classification performance even in cases of clinical samples of limited size. Accounting for concerns in relation to small sample sizes, our technique represents a meaningful step forward in terms of pattern recognition methodology, particularly when it is applied to diagnostic classifications of neurodevelopmental disorders. Besides, the proposed technique has been tested with other available benchmark datasets. The experimental outcomes showed that the accuracy of the classification that used virtual samples was superior to the one that used original training data without virtual samples.

SUBSPACES VERSUS SUBMANIFOLDS: A COMPARATIVE STUDY IN SMALL SAMPLE SIZE PROBLEM

2009 ◽  
Vol 23 (03) ◽  
pp. 463-490 ◽  
Author(s):  
HONG HUANG ◽  
JIANWEI LI ◽  
HAILIANG FENG
Keyword(s):  
Face Recognition ◽  
Comparative Study ◽  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Locally Linear Embedding ◽  
Small Sample Size Problem ◽  
Size Problem ◽  
The Face ◽  
Locally Linear

Automatic face recognition is a challenging problem in the biometrics area, where the dimension of the sample space is typically larger than the number of samples in the training set and consequently the so-called small sample size problem exists. Recently, neuroscientists emphasized the manifold ways of perception, and showed the face images may reside on a nonlinear submanifold hidden in the image space. Many manifold learning methods, such as Isometric feature mapping, Locally Linear Embedding, and Locally Linear Coordination are proposed. These methods achieved the submanifold by collectively analyzing the overlapped local neighborhoods and all claimed to be superior to such subspace methods as Eigenfaces and Fisherfaces in terms of classification accuracy. However, in literature, no systematic comparative study for face recognition is performed among them. In this paper, we carry out a comparative study in face recognition among them, and the study considers theoretical aspects as well as simulations performed using CMU PIE and FERET face databases.

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