Regularized discriminant analysis for the small sample size problem in face recognition
Pattern Recognition Letters
A new method to help diagnose cancers for small sample size
Expert Systems with Applications: An International Journal
Similarity classifier in diagnosis of bladder cancer
Computer Methods and Programs in Biomedicine
Kernel quadratic discriminant analysis for small sample size problem
Pattern Recognition
Medical data mining by fuzzy modeling with selected features
Artificial Intelligence in Medicine
Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification
IEEE Transactions on Neural Networks
Optimal training subset in a support vector regression electric load forecasting model
Applied Soft Computing
A new approach for manufacturing forecast problems with insufficient data: the case of TFT---LCDs
Journal of Intelligent Manufacturing
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
This paper proposes feature selection as an approach to deal with a bladder cancer data set with small sample size. Three feature selection methods and four classifiers were used to determine the best feature subsets that produce perfect classification accuracy. The smallest best feature subsets are used to build neural models with the small data set to achieve 100% training and testing accuracies. Therefore, the mega-trend-diffusion technique proposed by Li et al. to produce artificial samples is actually unnecessary. The similarity classifier proposed by Luukka was also applied to the small data set with the smallest best feature subsets to achieve 100% accuracy using only 4 samples (two with bladder cancer and two normal) for two selected p and m values. Given the same accuracy, using the best feature subsets selected is better than using all 13 features as done by Luukka. Furthermore, several indexes/methods commonly used in filtering feature selection methods were tested for their ability to find the best feature subsets for this particular small bladder cancer data set.