Improved SVM for Learning Multi-Class Domains with ROC Evaluation

Zhang, Xiaolong; Jiang, Chuan

doi:10.1109/icmlc.2007.4370641

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Model performance was evaluated using widely applied statistics, namely the area under the receiver-operator characteristics (ROC) curve or AUC statistic. The area under the ROC curve (AUC) has been used as a criterion to measure the performance of the classification algorithms even if the training data embraces an unbalanced class distribution and cost-sensitiveness [30]. In each class, the ROC curve applies the threshold values to the output values so that for each threshold, the true-positive ratio (TPR) and the false-positive ratio (FPR) values are simplified.…”

Section: Methodsmentioning

confidence: 99%

A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

Tamang¹,

Sharif²,

Al-Bayatti

et al. 2020

Symmetry

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The daily commute represents a source of chronic stress that is positively correlated with physiological consequences, including increased blood pressure, heart rate, fatigue, and other negative mental and physical health effects. The purpose of this research is to investigate and predict the physiological effects of commuting in Greater London on the human body based on machine-learning approaches. For each participant, the data were collected for five consecutive working days, before and after the commute, using non-invasive wearable biosensor technology. Multimodal behaviour, analysis and synthesis are the subjects of major efforts in computing field to realise the successful human–human and human–agent interactions, especially for developing future intuitive technologies. Current analysis approaches still focus on individuals, while we are considering methodologies addressing groups as a whole. This research paper employs a pool of machine-learning approaches to predict and analyse the effect of commuting objectively. Comprehensive experimentation has been carried out to choose the best algorithmic structure that suit the problem in question. The results from this study suggest that whether the commuting period was short or long, all objective bio-signals (heat rate and blood pressure) were higher post-commute than pre-commute. In addition, the results match both the subjective evaluation obtained from the Positive and Negative Affect Schedule and the proposed objective evaluation of this study in relation to the correlation between the effect of commuting on bio-signals. Our findings provide further support for shorter commutes and using the healthier or active modes of transportation.

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Section: Methodsmentioning

confidence: 99%

A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

Tamang¹,

Sharif²,

Al-Bayatti

et al. 2020

Symmetry

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“…For constructing the validation set, we randomly select one sample of each subject from the remaining samples of all the trained subjects. We use the one-versus-all strategy to transform the multi-classification problem to several two-classification problems [28]. We repeat the above steps, adding occlusion to the validation set as Symmetry 2020, 12, 78 20 of 21 described in Sections 7.2 and 7.3, respectively.…”

Section: Cs Csmentioning

confidence: 99%

“…We repeat the above steps, adding occlusion to the validation set as Symmetry 2020, 12, 78 20 of 21 described in Sections 7.2 and 7.3, respectively. Finally, we perform constrained optimization using the GlobalSearch framework provided by MATLAB to maximize the average area under receiver operating characteristic (ROC) curve of transformed two-classification problems [28].…”

Section: Cs Csmentioning

confidence: 99%

PointNet++ and Three Layers of Features Fusion for Occlusion Three-Dimensional Ear Recognition Based on One Sample per Person

Zhu

Zhang

2020

Symmetry

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The ear’s relatively stable structure makes it suitable for recognition. In common identification applications, only one sample per person (OSPP) is registered in a gallery; consequently, effectively training deep-learning-based ear recognition approach is difficult. The state-of-the-art (SOA) 3D ear recognition using the OSPP approach bottlenecks when large occluding objects are close to the ear. Hence, we propose a system that combines PointNet++ and three layers of features that are capable of extracting rich identification information from a 3D ear. Our goal is to correctly recognize a 3D ear affected by a large nearby occlusion using one sample per person (OSPP) registered in a gallery. The system comprises four primary components: (1) segmentation; (2) local and local joint structural (LJS) feature extraction; (3) holistic feature extraction; and (4) fusion. We use PointNet++ for ear segmentation. For local and LJS feature extraction, we propose an LJS feature descriptor–pairwise surface patch cropped using a symmetrical hemisphere cut-structured histogram with an indexed shape (PSPHIS) descriptor. Furthermore, we propose a local and LJS matching engine based on the proposed LJS feature descriptor and SOA surface patch histogram indexed shape (SPHIS) local feature descriptor. For holistic feature extraction, we use a voxelization method for global matching. For the fusion component, we use a weighted fusion method to recognize the 3D ear. The experimental results demonstrate that the proposed system outperforms the SOA normalization-free 3D ear recognition methods using OSPP when the ear surface is influenced by a large nearby occlusion.

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“…However, extending the AUC to multiclass problem is still an open research topic. Directly calculating the Volume under ROC (VUC) surface [19] is complicated while many studies [20,21] tried to estimate the volume by projecting it into multiple 2-class dimensions.…”

Section: Volume Under Roc (Multiclass Auc)mentioning

confidence: 99%

Robust multiclass classification for learning from imbalanced biomedical data

Phoungphol

Zhang

Zhao

2012

Tinshhua Sci. Technol.

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Imbalanced data is a common and serious problem in many biomedical classification tasks. It causes a bias on the training of classifiers and results in lower accuracy of minority classes prediction. This problem has attracted a lot of research interests in the past decade. Unfortunately, most research efforts only concentrate on 2-class problems. In this paper, we study a new method of formulating a multiclass Support Vector Machine (SVM) problem for imbalanced biomedical data to improve the classification performance. The proposed method applies cost-sensitive approach and ramp loss function to the Crammer and Singer multiclass SVM formulation. Experimental results on multiple biomedical datasets show that the proposed solution can effectively cure the problem when the datasets are noisy and highly imbalanced.

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Improved SVM for Learning Multi-Class Domains with ROC Evaluation

Cited by 7 publications

References 12 publications

A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

PointNet++ and Three Layers of Features Fusion for Occlusion Three-Dimensional Ear Recognition Based on One Sample per Person

Robust multiclass classification for learning from imbalanced biomedical data

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