Bootstrap Techniques for Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Floating search methods in feature selection
Pattern Recognition Letters
Machine Learning
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Comparison of Non-Parametric Methods for Assessing Classifier Performance in Terms of ROC Parameters
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
Expert Systems with Applications: An International Journal
Cross-validation and bootstrapping are unreliable in small sample classification
Pattern Recognition Letters
A bootstrap-based aggregate classifier for model-based clustering
Computational Statistics
A new feature selection method for Gaussian mixture clustering
Pattern Recognition
Feature selection with dynamic mutual information
Pattern Recognition
Advances in Feature Selection with Mutual Information
Similarity-Based Clustering
Feature subset selection in large dimensionality domains
Pattern Recognition
Conditional confidence intervals for classification error rate
Computational Statistics & Data Analysis
Indexes for three-class classification performance assessment: an empirical comparison
IEEE Transactions on Information Technology in Biomedicine
Impact of error estimation on feature selection
Pattern Recognition
Computational Statistics & Data Analysis
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative semi-supervised feature selection via manifold regularization
IEEE Transactions on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
An empirical comparison of nine pattern classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Feature Selection Using Probabilistic Prediction of Support Vector Regression
IEEE Transactions on Neural Networks
Compressive sensing and adaptive direct sampling in hyperspectral imaging
Digital Signal Processing
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We address two fundamental design issues of a classification system: the choice of the classifier and the dimensionality of the optimal feature subset. Resampling techniques are applied to estimate both the probability distribution of the misclassification rate (or any other figure of merit of a classifier) subject to the size of the feature set, and the probability distribution of the optimal dimensionality given a classification system and a misclassification rate. The latter allows for the estimation of confidence intervals for the optimal feature set size. Based on the former, a quality assessment for the classifier performance is proposed. Traditionally, the comparison of classification systems is accomplished for a fixed feature set. However, a different set may provide different results. The proposed method compares the classifiers independently of any pre-selected feature set. The algorithms are tested on 80 sets of synthetic examples and six standard databases of real data. The simulated data results are verified by an exhaustive search of the optimum and by two feature selection algorithms for the real data sets.