The nature of statistical learning theory
The nature of statistical learning theory
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Linear Programming Boosting via Column Generation
Machine Learning
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Structural Modelling with Sparse Kernels
Machine Learning
Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Generalized Analytic Rule Extraction for Feedforward Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Microarray data mining: facing the challenges
ACM SIGKDD Explorations Newsletter
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
A theoretical characterization of linear SVM-based feature selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Hybrid SOM-SVM Method for Analyzing Zebra Fish Gene Expression
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Rule extraction from linear support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Predictive neural networks for gene expression data analysis
Neural Networks
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Semi-Supervised Mixture of Kernels via LPBoost Methods
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Artificial Intelligence in Medicine
SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
Neural Computation
Geometrical Properties of Nu Support Vector Machines with Different Norms
Neural Computation
Regulation probability method for gene selection
Pattern Recognition Letters
TreeDT: Tree Pattern Mining for Gene Mapping
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Artificial Intelligence in Medicine
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Learning interpretable SVMs for biological sequence classification
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue
Artificial Intelligence in Medicine
On connectionism, rule extraction, and brain-like learning
IEEE Transactions on Fuzzy Systems
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
Guest editorial: Integrative data mining in systems biology: from text to network mining
Artificial Intelligence in Medicine
Rule-Based Assistance to Brain Tumour Diagnosis Using LR-FIR
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Expert Systems with Applications: An International Journal
Data Mining Using Rules Extracted from SVM: An Application to Churn Prediction in Bank Credit Cards
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Automated classification of dopaminergic neurons in the rodent brain
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Representation and feature selection using multiple kernel learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Support vector regression based hybrid rule extraction methods for forecasting
Expert Systems with Applications: An International Journal
Feature selection for SVM via optimization of kernel polarization with Gaussian ARD kernels
Expert Systems with Applications: An International Journal
Intelligible support vector machines for diagnosis of diabetes mellitus
IEEE Transactions on Information Technology in Biomedicine
Colon cancer prediction with genetics profiles using evolutionary techniques
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Rule extraction from support vector machines: A review
Neurocomputing
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Evolution strategies based adaptive Lp LS-SVM
Information Sciences: an International Journal
Experiment specific expression patterns
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Expert Systems with Applications: An International Journal
HIS'12 Proceedings of the First international conference on Health Information Science
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
Rule extraction from support vector machines based on consistent region covering reduction
Knowledge-Based Systems
PMBC: Pattern mining from biological sequences with wildcard constraints
Computers in Biology and Medicine
A note on hyper ellipse method for classifying biological and medical data
Computers in Biology and Medicine
International Journal of Data Mining and Bioinformatics
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Objective: Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. Material and methods: A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. Results and conclusion: Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.