Cost-sensitive deep forest for price prediction

Ma, Chao; Liu, Zhenbing; Cao, Zhiguang; Song, Wen; Zhang, Jie; Zeng, Weiliang

doi:10.1016/j.patcog.2020.107499

Cited by 41 publications

(23 citation statements)

References 28 publications

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“…Therefore, our future work will focus on better feature extraction algorithm to address this issue. Moreover, we will also consider deep forest (Ma et al, 2020 ; Zhang et al, 2020 ) as classifier and deep neural networks (Wang et al, 2018 ) for microexpression recognition in the future.…”

Section: Discussionmentioning

confidence: 99%

A New Feature Extraction and Recognition Method for Microexpression Based on Local Non-negative Matrix Factorization

et al. 2020

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Microexpression is usually characterized by short duration and small action range, and the existing general expression recognition algorithms do not work well for microexpression. As a feature extraction method, non-negative matrix factorization can decompose the original data into different components, which has been successfully applied to facial recognition. In this paper, local non-negative matrix factorization is explored to decompose microexpression into some facial muscle actions, and extract features for recognition based on apex frame. However, the existing microexpression datasets fall short of samples to train a classifier with good generalization. The macro-to-micro algorithm based on singular value decomposition can augment the number of microexpressions, but it cannot meet non-negative properties of feature vectors. To address these problems, we propose an improved macro-to-micro algorithm to augment microexpression samples by manipulating the macroexpression data based on local non-negative matrix factorization. Finally, several experiments are conducted to verify the effectiveness of the proposed scheme, which results show that it has a higher recognition accuracy for microexpression compared with the related algorithms based on CK+/CASME2/SAMM datasets.

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Section: Discussionmentioning

confidence: 99%

A New Feature Extraction and Recognition Method for Microexpression Based on Local Non-negative Matrix Factorization

et al. 2020

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“…Las bases de datos no relacionales son las que, a diferencia de las relacionales, no tienen un identificador que sirva de relación entre un conjunto de datos y otros. La información se organiza normalmente mediante documentos y es muy útil cuando no se tiene un esquema exacto de lo que se va a almacenar 32 . Estas bases de datos no relacionales son un sistema de almacenamiento de información que se caracteriza por no usar el lenguaje SQL para las consultas.…”

Section: Bases De Datos No Relacionalesunclassified

“…LAFUENTE, Ainhoa, Que es el web Scraping, {En Línea}, {23 de octubre 2020} Disponible en ( https://aukera.es/blog/web-scraping/ ) p.3032 LAFUENTE, Ainhoa, Bases de datos relacionales vs. no relacionales: ¿qué es mejor?, {En Línea},{23 de octubre 2020 }Disponible en (https://aukera.es/blog/bases-de-datos-relacionales-vsno-relacionales/) p.30…”

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Cost-sensitive deep forest for price prediction

Liu

Cao

et al. 2020

Pattern Recognition

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“…In contrast to DNNs, DF has fewer hyper-parameters, does not require backpropagation, is easy to train with low computational costs, and works well even for only small-scale training data. Since its inception, the DF algorithm 15 has demonstrated excellent performance in a wide range of applications in diverse fields such as diagnosing schizophrenia 17 , price prediction 18 , image retrieval 19 , drug interactions 20 , COVID-19 detection from CT images 21 , hyperspectral image classification 22 , human age estimation from face images 23 , short-term load forecasting of power systems 24 , and emotion recognition 25 among others.…”

Section: Introductionmentioning

confidence: 99%

Deep Forest Model for Diagnosing COVID-19 From Routine Blood Tests

AlJame

Imtiaz

Ahmad

et al. 2021

Preprint

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The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and poses considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.

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Cost-sensitive deep forest for price prediction

Cited by 41 publications

References 28 publications

A New Feature Extraction and Recognition Method for Microexpression Based on Local Non-negative Matrix Factorization

A New Feature Extraction and Recognition Method for Microexpression Based on Local Non-negative Matrix Factorization

Cost-sensitive deep forest for price prediction

Deep Forest Model for Diagnosing COVID-19 From Routine Blood Tests

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