Energy-based model of least squares twin Support Vector Machines for human action recognition

Nasiri, Jalal A.; Charkari, Nasrollah Moghadam; Mozafari, Kourosh

doi:10.1016/j.sigpro.2014.04.010

Cited by 82 publications

(17 citation statements)

References 34 publications

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“…Recently, there have been relatively few studies that use LS-SVM to recognize activities using a triaxial accelerometer. Nasiri et al [30] addressed the Energy-Based Least Square Twin Support Vector Machine (ELS-TSVM) algorithm, which is an extended LS-SVM classifier that performs classification using two nonparallel hyper planes instead of a single hyper plane, which is used in the conventional SVM. ELS-TSVM was used to recognize activities using computer vision instead of a triaxial accelerometer.…”

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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“…Both quadratic programming (QP) problems in TSVM pair are formulated as a typical SVM. Reports have shown that TSVM is better than both SVM and GEPSVM [42][43][44]. Mathematically, the TSVM is constructed by solving the two QP problems min…”

Section: Twin Support Vector Machinementioning

confidence: 99%

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

et al. 2016

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(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256ˆ256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s "variance and entropy (VE)" features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our "DTCWT + VE + TSVM" obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible.

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“…The effectiveness of TWSVM over other existing classification approaches has been validated on various benchmark datasets. TWSVM has better generalization ability and faster computational speed due to which it has been applied to several real life applications such as intrusion detection [60,61], activity recognition [62], image denoising [63], emotion recognition [64], text classification [65], defect prediction [66,67], disease diagnosis [68,69], and speaker identification [70]. Consider a binary classification problem of " " size.…”

Section: Twin Support Vector Machinementioning

confidence: 99%

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

Tomar

Agarwal

2016

Advances in Artificial Intelligence

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In multiple instance learning (MIL) framework, an object is represented by a set of instances referred to as bag. A positive class label is assigned to a bag if it contains at least one positive instance; otherwise a bag is labeled with negative class label. Therefore, the task of MIL is to learn a classifier at bag level rather than at instance level. Traditional supervised learning approaches cannot be applied directly in such kind of situation. In this study, we represent each bag by a vector of its dissimilarities to the other existing bags in the training dataset and propose a multiple instance learning based Twin Support Vector Machine (MIL-TWSVM) classifier. We have used different ways to represent the dissimilarity between two bags and performed a comparative analysis of them. The experimental results on ten benchmark MIL datasets demonstrate that the proposed MIL-TWSVM classifier is computationally inexpensive and competitive with state-of-the-art approaches. The significance of the experimental results has been tested by using Friedman statistic and Nemenyi post hoc tests.

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Energy-based model of least squares twin Support Vector Machines for human action recognition

Cited by 82 publications

References 34 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

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

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