The Experts below are selected from a list of 38424 Experts worldwide ranked by ideXlab platform

Tengjiao Guo - One of the best experts on this subject based on the ideXlab platform.

  • the one against all partition based Binary Tree support vector machine algorithms for multi class classification
    Neurocomputing, 2013
    Co-Authors: Xiaowei Yang, Tengjiao Guo
    Abstract:

    The Binary Tree support vector machine (SVM) algorithm is one of the mainstream algorithms for multi-class classification in the fields of pattern recognition and machine learning. In order to reduce the training and testing time of one-against-all SVM (OAA-SVM) and reduced OAA-SVM (R-OAA-SVM), in this study, two OAA partition based Binary Tree SVM algorithms are proposed for multi-class classification. One is the single-space-mapped Binary Tree SVM (SBT-SVM) and the other is the multi-space-mapped Binary Tree SVM (MBT-SVM). In the proposed two algorithms, the best OAA partition is determined for each non-leaf node and the k-fold cross validation strategy is adopted to obtain the optimal classifiers. A set of experiments is conducted on nine UCI datasets and two face recognition datasets to demonstrate their performances. The results show that in term of testing accuracy, MBT-SVM is comparable with one-against-one SVM (OAO-SVM), R-OAA-SVM and OAA-SVM and superior to SBT-SVM. In term of testing time, MBT-SVM is superior to OAO-SVM, Binary Tree of SVM (BTS), R-OAA-SVM and OAA-SVM and slightly longer than SBT-SVM. In term of training time, MBT-SVM is superior to BTS, R-OAA-SVM and OAA-SVM and comparable with SBT-SVM. For the datasets with smaller class number and training sample number, the training time of MBT-SVM is comparable with that of OAO-SVM. For the datasets with larger class number or training sample number, in most cases, the training time of MBT-SVM is longer than that of OAO-SVM.

Sooyoung Lee - One of the best experts on this subject based on the ideXlab platform.

  • support vector machines with Binary Tree architecture for multi class classification
    International Conference on Neural Information Processing, 2004
    Co-Authors: Sungmoon Cheong, Sooyoung Lee
    Abstract:

    For multi-class classification with Support Vector Machines (SVMs) a Binary decision Tree architecture is proposed for computational efficiency. The proposed SVM- based Binary Tree takes advantage of both the efficient computation of the Tree architecture and the high classification accuracy of SVMs. A modified Self-Organizing Map (SOM), K- SOM (Kernel-based SOM), is introduced to convert the multi-class problems into Binary Trees, in which the Binary decisions are made by SVMs. For consistency between the SOM and SVM the K-SOM utilizes distance measures at the kernel space, not at the input space. Also, by allowing overlaps in the Binary decision Tree, it overcomes the performance degradation of the Tree structure, and shows classification accuracy comparable to those of the popular multi-class SVM approaches with "one-to-one" and "one-to-the others". Keywords—Support Vector Machine, multi-class classification, Self-Organizing Map, Binary decision Tree

H Dang - One of the best experts on this subject based on the ideXlab platform.

Zhiyan Shi - One of the best experts on this subject based on the ideXlab platform.

Xiaowei Yang - One of the best experts on this subject based on the ideXlab platform.

  • the one against all partition based Binary Tree support vector machine algorithms for multi class classification
    Neurocomputing, 2013
    Co-Authors: Xiaowei Yang, Tengjiao Guo
    Abstract:

    The Binary Tree support vector machine (SVM) algorithm is one of the mainstream algorithms for multi-class classification in the fields of pattern recognition and machine learning. In order to reduce the training and testing time of one-against-all SVM (OAA-SVM) and reduced OAA-SVM (R-OAA-SVM), in this study, two OAA partition based Binary Tree SVM algorithms are proposed for multi-class classification. One is the single-space-mapped Binary Tree SVM (SBT-SVM) and the other is the multi-space-mapped Binary Tree SVM (MBT-SVM). In the proposed two algorithms, the best OAA partition is determined for each non-leaf node and the k-fold cross validation strategy is adopted to obtain the optimal classifiers. A set of experiments is conducted on nine UCI datasets and two face recognition datasets to demonstrate their performances. The results show that in term of testing accuracy, MBT-SVM is comparable with one-against-one SVM (OAO-SVM), R-OAA-SVM and OAA-SVM and superior to SBT-SVM. In term of testing time, MBT-SVM is superior to OAO-SVM, Binary Tree of SVM (BTS), R-OAA-SVM and OAA-SVM and slightly longer than SBT-SVM. In term of training time, MBT-SVM is superior to BTS, R-OAA-SVM and OAA-SVM and comparable with SBT-SVM. For the datasets with smaller class number and training sample number, the training time of MBT-SVM is comparable with that of OAO-SVM. For the datasets with larger class number or training sample number, in most cases, the training time of MBT-SVM is longer than that of OAO-SVM.