Machine learning multi-classifiers for peptide classification.
Loris NanniAlessandra LuminiPublished in: Neural Comput. Appl. (2009)
Keyphrases
- machine learning
- decision trees
- machine learning methods
- machine learning algorithms
- supervised classification
- feature selection
- machine learning approaches
- classification systems
- classification algorithm
- text classification
- pattern recognition
- support vector
- classification process
- classification models
- rule based classifier
- classification method
- supervised learning
- class labels
- probabilistic classifiers
- svm classifier
- classification rate
- roc analysis
- classification accuracy
- classification decisions
- support vector machine
- supervised machine learning
- multiple classifier systems
- classifier combination
- learning algorithm
- accurate classification
- training samples
- training set
- higher classification accuracy
- naive bayes
- image classification
- data mining
- improves the classification accuracy
- decision boundary
- optimum path forest
- active learning
- feature vectors
- multi category
- individual classifiers
- model selection
- majority voting
- feature set
- k nearest neighbour
- ensemble classifier
- imbalanced data sets
- feature ranking
- combining classifiers
- accurate classifiers
- discriminative classifiers
- multiple classifiers
- classifier ensemble
- learning classifier systems
- class distribution
- support vector machine svm