MLTSVM: A novel twin support vector machine to multi-label learning

Chen, Wei-Jie; Shao, Yuan‐Hai; Li, Chun‐Na; Deng, Ning

doi:10.1016/j.patcog.2015.10.008

Cited by 133 publications

(49 citation statements)

References 42 publications

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“…To the best of authors' knowledge, the combination of CC with LightGBM is novel and, further, it has never been used to solve a user clustering problem for MIMO NOMA. Besides, we assess the performance of CC-LightGBM via numerical simulations and use two classical adaptation learning algorithms from the literature, ML-kNN [14] and ML-TSVM [15], as benchmarks.…”

Section: Contributionmentioning

confidence: 99%

User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees

et al. 2020

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Section: Contributionmentioning

confidence: 99%

User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees

et al. 2020

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“…Similarly to the case of feature extraction methods, FrImCla users can select among several supervised learning algorithms including SVM [58], KNN [59], Neural networks [60], Gradient Boost [61], Logistic Regression [62], Random Forest [63] and Extremely Randomised Trees [64] for single-label datasets. In the case of multi-label datasets, FrImCla users can employ all the aforementioned algorithms through the binary relevance [65], the classifier chain [66], and the label powerset [45] methods; and, additionally, the ML-KNN [67] and the MLTSVM [68] methods are provided. In both cases, the list of algorithms can be easily extended to incorporate other techniques.…”

Section: Classification Modelsmentioning

confidence: 99%

FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning Techniques

García-Domínguez¹,

Domínguez²,

Heras³

et al. 2020

IEEE Access

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Deep learning techniques are currently the state of the art approach to deal with image classification problems. Nevertheless, non-expert users might find challenging the use of these techniques due to several reasons, including the lack of enough images, the necessity of trying different models and conducting a thorough comparison of the results obtained with them, and the technical difficulties of employing different libraries, tools and special purpose hardware like GPUs. In this work, we present FrImCla, an open-source and free tool that simplifies the construction of robust models for image classification from a dataset of images, and only using the computer CPU. Given a dataset of annotated images, FrImCla automatically constructs a classification model (both for single-label and multi-label classification problems) by trying several feature extractors (based both on transfer learning and traditional computer vision methods) and machine learning algorithms, and selecting the best combination after a thorough statistical analysis. Thus, this tool can be employed by non-expert users to create accurate models from small datasets of images without requiring any special purpose hardware. In addition, in this paper, we show that FrImCla can be employed to construct accurate models that are close, or even better, to the state-of-the-art models. INDEX TERMS AutoML, deep learning, image classification, transfer learning.

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“…It avoids the network structure selection, overlearning and underlearning, and other problems in artificial neural network algorithm. However, standard SVM is a binary classifier so that it cannot effectively solve multiclass classification [33]. Therefore, to overcome the shortages of standard SVM classifiers, many researchers tried to modify and improve SVM, such as "oneagainst-one" [34], "one-against-all" [35], and "decision tree" [36] which is suitable for multiclass classification.…”

Section: Fault Diagnosis Based On Sphere-structured Support Vector Mamentioning

confidence: 99%

Power Transformer Partial Discharge Fault Diagnosis Based on Multidimensional Feature Region

Jia

Xie

Wang

et al. 2016

Mathematical Problems in Engineering

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Effectively extracting power transformer partial discharge (PD) signals feature is of great significance for monitoring power transformer insulation condition. However, there has been lack of practical and effective extraction methods. For this reason, this paper suggests a novel method for the PD signal feature extraction based on multidimensional feature region. Firstly, in order to better describe differences in each frequency band of fault signals, empirical mode decomposition (EMD) and Hilbert-Huang transform (HHT) band-pass filter wave for raw signal is carried out. And the component of raw signals on each frequency band can be obtained. Secondly, the sample entropy value and the energy value of each frequency band component are calculated. Using the difference of each frequency band energy and complexity, signals feature region is established by the multidimensional energy parameters and the multidimensional sample entropy parameters to describe PD signals multidimensional feature information. Finally, partial discharge faults are classified by sphere-structured support vector machines algorithm. The result indicates that this method is able to identify and classify different partial discharge faults.

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MLTSVM: A novel twin support vector machine to multi-label learning

Cited by 133 publications

References 42 publications

User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees

User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees

FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning Techniques

Power Transformer Partial Discharge Fault Diagnosis Based on Multidimensional Feature Region

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