A vector-valued support vector machine model for multiclass problem

Wang, Ran; Kwong, Sam; Chen, Degang; Cao, Jingjing

doi:10.1016/j.ins.2013.02.001

Cited by 19 publications

(5 citation statements)

References 28 publications

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“…The LS-SVM for multiclassification is decomposed into multiple binary classification tasks. The LS-SVM for multiclassification reduces the computational complexity by using a small number of classifiers and effectively eliminates the unclassifiable regions that possibly affect the classification performance of this algorithm [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework

Zheng

2015

Journal of Electrical and Computer Engineering

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Human activity recognition via triaxial accelerometers can provide valuable information for evaluating functional abilities. In this paper, we present an accelerometer sensor-based approach for human activity recognition. Our proposed recognition method used a hierarchical scheme, where the recognition of ten activity classes was divided into five distinct classification problems. Every classifier used the Least Squares Support Vector Machine (LS-SVM) and Naive Bayes (NB) algorithm to distinguish different activity classes. The activity class was recognized based on the mean, variance, entropy of magnitude, and angle of triaxial accelerometer signal features. Our proposed activity recognition method recognized ten activities with an average accuracy of 95.6% using only a single triaxial accelerometer.

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Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework

Zheng

2015

Journal of Electrical and Computer Engineering

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“…79 (5.18) 7[4[1[3[2[5,6] [3[4[6[5[9[1,8] To investigate performance in various situations, performance of the proposed method was evaluated for five datasets which are described in Table 1. The continued effectiveness of the proposed method was compared with OAO, DDAG, DT, and VVD (Wang et al 2013). Each row of Table 3 demonstrates performance of each algorithm for each dataset, and the best performance for each dataset is shown in italic.…”

Section: Experiments Data and Obtained Resultsmentioning

confidence: 99%

Multiclass Classification Based on Multi-criteria Decision-making

Baloochian

Ghaffary

2019

J Classif

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Lots of real-world problems require multiclass classification. Since most general classification methods are originally introduced for binary problems (including two classes), they should be extended to multiclass problems. A solution proposed for multiclass problems is to decompose such problems to several binary ones and then combine the results obtained from smaller problems as a tree-based structure to obtain the final solution. In this study, a novel method which uses VlseKriterijumska optimizacija I Kompromisno Resenje multi-criteria decisionmaking was proposed to build the best directed binary tree with minimum error. The proposed method is independent of classifier; nevertheless, in the current experiments, the support vector machine was employed as the base classifier. The proposed method was tested on datasets and the results were compared with other methods. It can be seen that it improves precision of predictions significantly.

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“…The base classifiers in all the compared methods are binary, thus support vector machine (SVM) [53,56,57] is used consistently, since it has been verified to be ideal binary model in many applications. Furthermore, non-linear SVM is realized by applying RBF kernel K(x,…”

Section: Methodsmentioning

confidence: 99%

Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain

Wang

2019

Preprint

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Classifier chain (CC) is a multi-label learning approach that constructs a sequence of binary classifiers according to a label order. Each classifier in the sequence is responsible for predicting the relevance of one label. When training the classifier for a label, proceeding labels will be taken as extended features. If the extended features are highly correlated to the label, the performance will be improved, otherwise, the performance will not be influenced or even degraded. How to discover label correlation and determine the label order is critical for CC approach. This paper employs Bayesian network (BN) to model the label correlations and proposes a new BN-based CC method (BNCC). First, conditional entropy is used to describe the dependency relations among labels. Then, a BN is built up by taking nodes as labels and weights of edges as their dependency relations. A new scoring function is proposed to evaluate a BN structure, and a heuristic algorithm is introduced to optimize the BN. At last, by applying topological sorting on the nodes of the optimized BN, the label order for constructing CC model is derived. Experimental comparisons demonstrate the feasibility and effectiveness of the proposed method.

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A vector-valued support vector machine model for multiclass problem

Cited by 19 publications

References 28 publications

Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework

Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework

Multiclass Classification Based on Multi-criteria Decision-making

Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain

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