A novel MF-DFA-Phase-Field hybrid MRIs classification system

Wang, Jian; Xu, Heming; Jiang, Wenjing; Han, Ziwei; Kim, Junseok

doi:10.1016/j.eswa.2023.120071

Cited by 3 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the characteristics of phase field phase separation, the phase field model can form an interface-like hyperplane and the exciting thing is that the interface is nonlinear. Therefore, based on the phase field model, novel classifiers were constructed by Wang et al [37,38], which had an excellent classification performance. Due to the ability of the phase field model to generate stable nonlinear boundaries during model evolution, it is particularly well-suited for handling complex emotional expressions and contexts that are often found in textual data in sentiment classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Hotel Comment Emotion Classification Based on the MF-DFA and Partial Differential Equation Classifier

Duanzhu,

Wang,

Jia

2023

Fractal Fract

Self Cite

View full text Add to dashboard Cite

Due to the significant value that hotel reviews hold for both consumers and businesses, the development of an accurate sentiment classification method is crucial. By effectively distinguishing the authenticity of reviews, consumers can make informed decisions, and businesses can gain insights into customer feedback to improve their services and enhance overall competitiveness. In this paper, we propose a partial differential equation model based on phase-field for sentiment analysis in the field of hotel comment texts. The comment texts are converted into word vectors using the Word2Vec tool, and then we utilize the multifractal detrended fluctuation analysis (MF-DFA) model to extract the generalized Hurst exponent of the word vector time series to achieve dimensionality reduction of the word vector data. The dimensionality reduced data are represented in a two-dimensional computational domain, and the modified Allen–Cahn (AC) function is used to evolve the phase values of the data to obtain a stable nonlinear boundary, thereby achieving automatic classification of hotel comment texts. The experimental results show that the proposed method can effectively classify positive and negative samples and achieve excellent results in classification indicators. We compared our proposed classifier with traditional machine learning models and the results indicate that our method possesses a better performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Hotel Comment Emotion Classification Based on the MF-DFA and Partial Differential Equation Classifier

Duanzhu,

Wang,

Jia

2023

Fractal Fract

Self Cite

View full text Add to dashboard Cite

show abstract

A novel binary data classification algorithm based on the modified reaction–diffusion predator–prey system with Holling-II function

Chen,

Wang

2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

In this study, we propose a modified reaction–diffusion prey–predator model with a Holling-II function for binary data classification. In the model, we use u and v to represent the densities of prey and predators, respectively. We modify the original equation by substituting the term v with f−v to obtain a stable and clear nonlinear decision surface. By employing a finite difference method for numerical solution of the original model, we conduct various experiments in two-dimensional and three-dimensional spaces to validate the feasibility of the classifier. Additionally, with consideration for wide real applications, we perform classification experiments on electroencephalogram signals, demonstrating the effectiveness and robustness of the classifier in binary data classification.

show abstract

Hotel Comment Emotion Classification Based on the MF-DFA and Partial Differential Equation Classifier

Duanzhu¹,

Wang²,

Jia³

2023

Preprint

View full text Add to dashboard Cite

In this paper, we propose a partial differential equation model based on phase-field for sentiment analysis in the field of hotel comment texts. The comment texts are converted into word vectors using the Word2Vec tool, and then we utilize the multifractal detrended fluctuation analysis (MF-DFA) model to extract the generalized Hurst exponent of the word vector time series to achieve dimensionality reduction of the word vector data. The dimensionality reduced data is represented in a two-dimensional computational domain, and the modified Allen-Cahn (AC) function is used to evolve the phase values of the data to obtain a stable nonlinear boundary, thereby achieving automatic classification of hotel comment texts. The experimental results show that the proposed method can effectively classify positive and negative samples and achieve excellent results in classification indicators. We compared our proposed classifier with traditional machine learning models and the results indicate that our method possesses a better performance.

show abstract

A novel MF-DFA-Phase-Field hybrid MRIs classification system

Cited by 3 publications

References 32 publications

Hotel Comment Emotion Classification Based on the MF-DFA and Partial Differential Equation Classifier

Hotel Comment Emotion Classification Based on the MF-DFA and Partial Differential Equation Classifier

A novel binary data classification algorithm based on the modified reaction–diffusion predator–prey system with Holling-II function

Hotel Comment Emotion Classification Based on the MF-DFA and Partial Differential Equation Classifier

Contact Info

Product

Resources

About