The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Classifier Ensembles with a Random Linear Oracle
IEEE Transactions on Knowledge and Data Engineering
A new classification model with simple decision rule for discovering optimal feature gene pairs
Computers in Biology and Medicine
From dynamic classifier selection to dynamic ensemble selection
Pattern Recognition
A review of feature selection techniques in bioinformatics
Bioinformatics
Novel Extension of k - TSP Algorithm for Microarray Classification
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Weighted Top Score Pair Method for Gene Selection and Classification
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Microarray data classification based on ensemble independent component selection
Computers in Biology and Medicine
Pyevolve: a Python open-source framework for genetic algorithms
ACM SIGEVOlution
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Pattern Recognition and Information Processing Using Neural Networks;Guest Editors: Fuchun Sun,Ying Tan,Cong Wang
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Scikit-learn: Machine Learning in Python
The Journal of Machine Learning Research
Ensemble classification of colon biopsy images based on information rich hybrid features
Computers in Biology and Medicine
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In this paper, a genetic algorithm (GA) based ensemble support vector machine (SVM) classifier built on gene pairs (GA-ESP) is proposed. The SVMs (base classifiers of the ensemble system) are trained on different informative gene pairs. These gene pairs are selected by the top scoring pair (TSP) criterion. Each of these pairs projects the original microarray expression onto a 2-D space. Extensive permutation of gene pairs may reveal more useful information and potentially lead to an ensemble classifier with satisfactory accuracy and interpretability. GA is further applied to select an optimized combination of base classifiers. The effectiveness of the GA-ESP classifier is evaluated on both binary-class and multi-class datasets.