The nature of statistical learning theory
The nature of statistical learning theory
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved branch and bound algorithm for feature selection
Pattern Recognition Letters
An SVM-based Algorithm for Identification of Photosynthesis-specific Genome Features
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature ranking and best feature subset using mutual information
Neural Computing and Applications
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Fuzzy support vector machine for multi-class text categorization
Information Processing and Management: an International Journal
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
Multiclass support vector machines for diagnosis of erythemato-squamous diseases
Expert Systems with Applications: An International Journal
Real time face and mouth recognition using radial basis function neural networks
Expert Systems with Applications: An International Journal
Breast mass classification based on cytological patterns using RBFNN and SVM
Expert Systems with Applications: An International Journal
Multi-class support vector machine for classification of the ultrasonic images of supraspinatus
Expert Systems with Applications: An International Journal
Feature selection with dynamic mutual information
Pattern Recognition
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Orthogonal forward selection and backward elimination algorithms for feature subset selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines
Artificial Intelligence in Medicine
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Computers & Mathematics with Applications
An automatic computer-aided diagnosis system for liver tumours on computed tomography images
Computers and Electrical Engineering
Advanced Engineering Informatics
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This paper proposed an effort to apply the several multi-class classifiers that are the maximum likelihood classifier, the radial basis function neural network, the fuzzy support vector machine and the error correcting output codes method to classify the ultrasonic supraspinatus images. The maximum mutual information criterion is adopted to search for the powerful features generating from the first order histogram statistics, gray-level co-occurrence matrix and texture feature coding method. In experiments, the most commonly used performance measures including the accuracy, sensitivity, accuracy and F_score are applied to evaluate the classification of the four classifiers. In addition, the Youden's index, the discriminant power and the area of receiver operating characteristics curve are also used to analyze the classification capability. The experimental results demonstrate that the implementation of radial bass function neural network can achieve 94.1% classification accuracy and performance measures are significantly superior to the others.