The nature of statistical learning theory
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An introduction to support Vector Machines: and other kernel-based learning methods
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Structural Modelling with Sparse Kernels
Machine Learning
Integrating Microarray Data by Consensus Clustering
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Microarray data mining: facing the challenges
ACM SIGKDD Explorations Newsletter
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Classification and knowledge discovery in protein databases
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Predictive neural networks for gene expression data analysis
Neural Networks
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Artificial Intelligence in Medicine
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 02
Gene subset selection in kernel-induced feature space
Pattern Recognition Letters
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Artificial Intelligence in Medicine
Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A three-stage framework for gene expression data analysis by L1-norm support vector regression
International Journal of Bioinformatics Research and Applications
Evolution strategies based adaptive Lp LS-SVM
Information Sciences: an International Journal
Leukemia prediction from gene expression data—a rough set approach
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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
Efficient hyperkernel learning using second-order cone programming
IEEE Transactions on Neural Networks
An enhanced classifier fusion model for classifying biomedical data
International Journal of Computational Vision and Robotics
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
A Simple and Fast Multi-instance Classification via Support Vector Machine
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
An information theoretic sparse kernel algorithm for online learning
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Gene expression profiling using DNA microarray technique has been shown as a promising tool to improve the diagnosis and treatment of cancer. Recently, many computational methods have been used to discover maker genes, make class prediction and class discovery based on gene expression data of cancer tissue. However, those techniques fall short on some critical areas. These included (a) interpretation of the solution and extracted knowledge. (b) Integrating various sources data and incorporating the prior knowledge into the system. (c) Giving a global understanding of biological complex systems by a complete knowledge discovery framework. This paper proposes a multiple-kernel SVM based data mining system. Multiple tasks, including feature selection, data fusion, class prediction, decision rule extraction, associated rule extraction and subclass discovery, are incorporated in an integrated framework. ALL-AML Leukemia dataset is used to demonstrate the performance of this system.