Smooth pinball loss nonparallel support vector machine for robust classification

Liu, Ming-Zeng; Shao, Yuan‐Hai; Li, Chun‐Na; Chen, Weijie

doi:10.1016/j.asoc.2020.106840

Cited by 34 publications

(16 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A plethora of methods can be used for data classification. Some of the most common are probabilistic methods [20][21][22], decision trees [23][24][25], rule-based methods [26][27][28], support vector machine methods [29,30], instance-based methods, and neural networks [31,32].…”

Section: Classification Methodsmentioning

confidence: 99%

Ontology-Based Feature Selection: A Survey

2021

View full text Add to dashboard Cite

The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine-learning techniques, able to extract knowledge from information sources, and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction from various sources such as text, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic.

show abstract

Section: Classification Methodsmentioning

confidence: 99%

Ontology-Based Feature Selection: A Survey

2021

View full text Add to dashboard Cite

show abstract

“…Support Vector Machine (SVM) is an excellent classification algorithm. The basic idea of this algorithm is to find the best separation hyperplane in the feature space to maximize the interval between positive and negative samples in the training set [28]. This algorithm has a unique advantage in small sample and high-dimensional pattern recognition.…”

Section: Model Constructionmentioning

confidence: 99%

Feature Wavelength Selection Based on the Combination of Image and Spectrum for Aflatoxin B1 Concentration Classification in Single Maize Kernels

2022

View full text Add to dashboard Cite

Aflatoxin B1 (AFB1) is a very strong carcinogen, maize kernels are easily infected by this toxin during storage. Rapid and accurate identification of AFB1 is of great significance to ensure food safety. In this study, a novel method for classification of AFB1 in single maize kernels was developed. Four groups of maize kernel samples with different AFB1 concentrations (10, 20, 50, and 100 ppb) were prepared by artificial inoculation of toxin. In addition, one group of maize kernel samples without AFB1 were prepared as control, each group with 70 samples. The visible and short wave near-infrared (Vis-SWNIR) region (500–1000 nm) and long wave near-infrared (LWNIR) region (1000–2000 nm) hyperspectral images of all samples were obtained respectively, and the hyperspectral images in 500–2000 nm range was obtained after spectral pretreatment and fusion. Kennard-Stone algorithm was used to divide the samples into calibration set or prediction set. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to roughly select the characteristic wavelengths of the calibration set samples, and 25 and 26 effective wavelengths were obtained respectively. Based on the roughly selected wavelengths, a method of fine selection of the characteristic wavelengths was proposed by using the gray-value difference of image (GDI), and a few number of characteristic wavelengths were further selected. Under the LDA classification model, 10 characteristic wavelengths were selected to test the prediction set and the independent verification samples, and the ideal result were obtained with an accuracy of 94.46% and 91.11%, respectively. This study provides a new approach for AFB1 concentration classification of single maize kernels.

show abstract

“…Un método comúnmente utilizado en la clasificación es la máquina de vectores de soporte (SVM), que es un algoritmo supervisado de aprendizaje automático Liu et al (2020). El algoritmo intenta encontrar dos hiperplanos paralelos que maximicen la distancia mínima entre dos clases dentro de las muestras Liu et al (2020).…”

Section: Máquina De Vectores De Soporte (Svm)unclassified

Sistema híbrido inteligente para el control y operación de un convertidor elevador en modo Soft-Switching

Fernández-Serantes

Casteleiro‐Roca

Calvo-Rolle

2022

Rev. iberoam. autom. inform. ind.

View full text Add to dashboard Cite

En este trabajo de investigación se presenta una estrategia de control inteligente implementada en un convertidor elevador con topología de medio puente. El sistema se usa para asegurar que el convertidor funcione en modo "Soft-Switching". El primer paso es realizar el análisis del convertidor de potencia, mostrando los dos posibles modos de funcionamiento: "Hard-Switching" y "Soft-Switching". Posteriormente se implementa un modelo inteligente con el fin de identificar el modo de funcionamiento del convertidor. Este modelo se basa en un algoritmo de clasificación mediante técnicas inteligentes que es capaz de diferenciar entre los dos modos de funcionamiento. Se han obtenido muy buenos resultados de clasificación y una alta precisión, permitiendo la implementación del modelo en la estrategia de control del convertidor. La implementacion de este sistema permite asegurar que el convertidor funcione en el modo deseado: modo "Soft-Switching".

show abstract

Smooth pinball loss nonparallel support vector machine for robust classification

Cited by 34 publications

References 42 publications

Ontology-Based Feature Selection: A Survey

Ontology-Based Feature Selection: A Survey

Feature Wavelength Selection Based on the Combination of Image and Spectrum for Aflatoxin B1 Concentration Classification in Single Maize Kernels

Sistema híbrido inteligente para el control y operación de un convertidor elevador en modo Soft-Switching

Contact Info

Product

Resources

About