Journal ArticleDOI
Multiple-kernel SVM based multiple-task oriented data mining system for gene expression data analysis
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TLDR
This paper proposes a multiple-kernel SVM based data mining system where multiple tasks, including feature selection, data fusion, class prediction, decision rule extraction, associated rule extraction and subclass discovery are incorporated in an integrated framework.Abstract:
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.read more
Citations
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A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data
TL;DR: A framework with ensemble techniques is presented for customer churn prediction directly using longitudinal behavioral data and a novel approach called the hierarchical multiple kernel support vector machine (H-MK-SVM) is formulated.
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Kernel Association for Classification and Prediction: A Survey
TL;DR: This survey outlines the latest trends and innovations of a kernel framework for big data analysis and provides a useful overview of this evolving field for both specialists and relevant scholars.
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Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization
TL;DR: Two novel graph-regularizedNMF methods are proposed, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively, which significantly outperform state-of-the-art data representation methods.
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Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Zhen-Yu Chen,Zhi-Ping Fan +1 more
TL;DR: Computational results show that C-MK-SVM exhibits better customer behavior prediction performance and higher computational speed than support vector machine and multiple kernel support vectors machine.
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Scene classification of remote sensing image based on deep network and multi-scale features fusion
TL;DR: The multi-scale images and features of the convolutional and the fully-connected layers in the deep learning process are utilized to enhance the representation abilities of the classification features, so the classification result is better.
References
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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Journal ArticleDOI
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
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TL;DR: It is shown that there is diversity in gene expression among the tumours of DLBCL patients, apparently reflecting the variation in tumour proliferation rate, host response and differentiation state of the tumour.