Nonlinear optimization: complexity issues
Nonlinear optimization: complexity issues
The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
ACM Computing Surveys (CSUR)
Provably Fast Training Algorithms for Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Shrinkage estimator generalizations of Proximal Support Vector Machines
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An Effective Support Vector Machines (SVMs) Performance Using Hierarchical Clustering
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Data mining a diabetic data warehouse
Artificial Intelligence in Medicine
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Using a large national health database, we propose an enhanced SVM-based model called Hierarchical Clustering Support Vector Machine (HCSVM)that utilizes multiple levels of clusters to classify patients diagnosed with type-2diabetes. Multiple HCSVMs are trained for clusters at different levels of the hierarchy.Some clusters at certain levels of the hierarchy capture more separablesample spaces than the others. As a result, HCSVMs at different levels may developdifferent classification capabilities. Since the locations of the superiorSVMs are data dependent, the HCSVM model in this study takes advantage ofan adaptive strategy to select the most suitable HCSVM for classifying the testingsamples. This model solves the large data set problem inherent with the traditionalsingle SVM model because the entire data set is partitioned into smallerand more homogenous clusters. Other approaches also use clustering and multipleSVM to solve the problem of large datasets. These approaches typical employedonly one level of clusters. However, a single level of clusters may notprovide an optimal partition of the sample space for SVM trainings. On the contrary,HCSVMs utilize multiple partitions available in a multilevel tree to capturea more separable sample space for SVM trainings. Compared with the traditionalsingle SVM model and one-level multiple SVMs model, the HCSVM Modelmarkedly improves the accuracy for classifying testing samples.