Reordering adaptive directed acyclic graphs: an improved algorithm for multiclass support vector machines

Phetkaew, Thimaporn; Kijsirikul, Boonserm; Rivepiboon, Wanchai

doi:10.1109/ijcnn.2003.1223939

Cited by 18 publications

(16 citation statements)

References 2 publications

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“…This paper extends the ideas presented in [41,42], arguing that the pairs of classes that are easier to separate must be placed in the upper nodes of the hierarchy. However, we employ a simpler and more accurate method performing a more exhaustive experimentation and finding that there are in fact significant differences.…”

Section: Related Workmentioning

confidence: 65%

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Enhancing directed binary trees for multi-class classification

Montañés

Barranquero

Díez

et al. 2013

Information Sciences

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One approach to multi-class classification consists in decomposing the original problem into a collection of binary classification tasks. The outputs of these binary classifiers are combined to produce a single prediction. Winner-takesall, max-wins and tree voting schemes are the most popular methods for this purpose. However, tree schemes can deliver faster predictions because they need to evaluate less binary models. Despite previous conclusions reported in the literature, this paper shows that their performance depends on the organization of the tree scheme, i.e. the positions where each pairwise classifier is placed on the graph. Different metrics are studied for this purpose, proposing a new one that considers the precision and the complexity of each pairwise model, what makes the method to be classifier-dependent. The study is performed using Support Vector Machines (SVMs) as base classifiers, but it could be extended to other kind of binary classifiers. The proposed method, tested on benchmark data sets and on one real-world application, is able to improve the accuracy of other decomposition multi-class classifiers, producing even faster predictions.

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Section: Related Workmentioning

confidence: 65%

“…Similarly, Feng et al [17] employed the Jaakkola-Haussler error bound [4]. With regard to an ADAG, a structure is selected based on the hard margin error in [41].…”

Section: Related Workmentioning

confidence: 99%

Enhancing directed binary trees for multi-class classification

Montañés

Barranquero

Díez

et al. 2013

Information Sciences

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show abstract

“…In addition to these approaches above, some variants such as reordering DAGSVM [24], binary decision tree SVM [25], and error correcting codes SVM [26] have appeared in the literature. These novel methods have a wide range of applications including speech recognition, fault diagnosis, system discrimination, financial engineering and bioinformatics, etc.…”

Section: Multiclass Support Vector Machinesmentioning

confidence: 99%

Multiple Sensors Based Hand Motion Recognition Using Adaptive Directed Acyclic Graph

Xue

Xiang

et al. 2017

Applied Sciences

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Abstract:The use of human hand motions as an effective way to interact with computers/robots, robot manipulation learning and prosthetic hand control is being researched in-depth. This paper proposes a novel and effective multiple sensor based hand motion capture and recognition system. Ten common predefined object grasp and manipulation tasks demonstrated by different subjects are recorded from both the human hand and object points of view. Three types of sensors, including electromyography, data glove and FingerTPS are applied to simultaneously capture the EMG signals, the finger angle trajectories, and the contact force. Recognising different grasp and manipulation tasks based on the combined signals is investigated by using an adaptive directed acyclic graph algorithm, and results of comparative experiments show the proposed system with a higher recognition rate compared with individual sensing technology, as well as other algorithms. The proposed framework contains abundant information from multimodal human hand motions with the multiple sensor techniques, and it is potentially applicable to applications in prosthetic hand control and artificial systems performing autonomous dexterous manipulation.

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“…To overcome this problem, several authors have proposed to use an Adaptive DAG (called ADAG) by optimizing its structure. However, the generalization ability still depends on the structure of the tree [26,4,20,32,40]. We propose a new elimination decoding which takes into account all the outputs of the binary classifiers.…”

Section: Elimination Decodingmentioning

confidence: 99%

Comparing Combination Rules of Pairwise Neural Networks Classifiers

Lézoray

Cardot

2007

Neural Process Lett

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Abstract. A decomposition approach to multiclass classification problems consists in decomposing a multiclass problem into a set of binary ones. Decomposition splits the complete multiclass problem into a set of smaller classification problems involving only two classes (binary classification: dichotomies). With a decomposition, one has to define a recombination which recomposes the outputs of the dichotomizers in order to solve the original multiclass problem. There are several approaches to the decomposition, the most famous ones being one-againt-all and one-against-one also called pairwise. In this paper, we focus on pairwise decomposition approach to multiclass classification with neural networks as the base learner for the dichotomies. We are primarily interested in the different possible ways to perform the so-called recombination (or decoding). We review standard methods used to decode the decomposition generated by a one-against-one approach. New decoding methods are proposed and compared to standard methods. A stacking decoding is also proposed which consists in replacing the whole decoding or a part of it by a trainable classifier to arbiter among the conflicting predictions of the pairwise classifiers. Proposed methods try to cope with the main problem while using pairwise decomposition: the use of irrelevant classifiers. Substantial gain is obtained on all datasets used in the experiments. Based on the above, we provide future research directions which consider the recombination problem as an ensemble method.

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Reordering adaptive directed acyclic graphs: an improved algorithm for multiclass support vector machines

Cited by 18 publications

References 2 publications

Enhancing directed binary trees for multi-class classification

Enhancing directed binary trees for multi-class classification

Multiple Sensors Based Hand Motion Recognition Using Adaptive Directed Acyclic Graph

Comparing Combination Rules of Pairwise Neural Networks Classifiers

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