Enhancement of multi-class support vector machine construction from binary learners using generalization performance

Songsiri, Patoomsiri; Phetkaew, Thimaporn; Kijsirikul, Boonserm

doi:10.1016/j.neucom.2014.09.021

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…As described in Algorithm 2, IB-DTree constructs a tree using a recursive procedure starting from the root node from lines 1-7 with all candidate classes in line 2. The node-adding procedure will be processed from lines [8][9][10][11][12][13][14][15][16][17][18][19]. First, the initial classifier h with the lowest entropy will be selected.…”

Section: The Information-based Decision Treementioning

confidence: 99%

“…Generalization error can be estimated directly using k-fold cross-validation and used to compare the performance of binary classifiers, but it consumes a high computational cost. Another method to estimate the generalization error is by using Inequation 2 with the appropriate parameter substitution [19]. Using the latter method, we can compare relative Algorithm 3 Information-Based and Generalization-Error Estimation Decision Tree SVM (IBGE-DTree)…”

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confidence: 99%

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Efficient Decision Trees for Multi–Class Support Vector Machines Using Entropy and Generalization Error Estimation

Kantavat

Kijsirikul

Songsiri

et al. 2018

International Journal of Applied Mathematics and Computer Science

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We propose new methods for Support Vector Machines (SVMs) using tree architecture for multi-class classification. In each node of the tree, we select an appropriate binary classifier using entropy and generalization error estimation, then group the examples into positive and negative classes based on the selected classifier and train a new classifier for use in the classification phase. The proposed methods can work in time complexity between O(log 2 N ) to O(N ) where N is the number of classes. We compared the performance of our proposed methods to the traditional techniques on the UCI machine learning repository using 10-fold cross-validation. The experimental results show that our proposed methods are very useful for the problems that need fast classification time or problems with a large number of classes as the proposed methods run much faster than the traditional techniques but still provide comparable accuracy.

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Section: The Information-based Decision Treementioning

confidence: 99%

mentioning

confidence: 99%

Efficient Decision Trees for Multi–Class Support Vector Machines Using Entropy and Generalization Error Estimation

Kantavat

Kijsirikul

Songsiri

et al. 2018

International Journal of Applied Mathematics and Computer Science

Self Cite

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“…A more recent work has utilised DAG structures to solve the multiclass classification problem (Songsiri, Phetkaew & Kijsirikul, 2015). More specifically the DAG structure used to combine the prediction results obtained from a set of binary classifiers, which can be considered a special case of using a set of binary classifiers to solve the multi-class classification problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A Directed Acyclic Graph (DAG) Ensemble Classification Model

Alshdaifat

Coenen

Dures

2017

International Journal of Data Warehousing and Mining

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In this paper a hierarchical ensemble classification approach, that utilizes a Directed Acyclic Graph (DAG) structure, is proposed as a solution to the multi-class classification problem. Two main DAG structures are considered: (i) rooted DAG, and (ii) non-rooted DAG. The main challenges that are considered in this paper are: (i) the successive misclassification issue associated with hierarchical classification, and (i) identification of the starting node within the non-rooted DAG approach. To address these issues the idea is to utilize Bayesian probability values to: select the best starting DAG node, and to dictate whether single or multiple paths should be followed within the DAG structure. The reported experimental results indicated that the proposed DAG structure is more effective than when using a simple binary tree structure for generating a hierarchical classification model.

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