Distance Variance Score: An Efficient Feature Selection Method in Text Classification

Wang, Heyong; Hong, Minghui

doi:10.1155/2015/695720

Cited by 14 publications

(9 citation statements)

References 28 publications

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“…The Laplacian technique simply sharpens an image by emphasizing the high gradient information in an image [26]. In addition, the Laplacian score is proved discriminative in the application of several areas [27]. The Laplacian score for blur detection is used as the examination criteria in this study.…”

Section: Blur Detection and Laplace Operatormentioning

confidence: 99%

Applying t-SNE to Estimate Image Sharpness of Low-cost Nailfold Capillaroscopy

Wang¹,

Chen²

2022

Intelligent Automation &Amp; Soft Computing

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Machine learning can classify the image clarity of low-cost nailfold capillaroscopy (NC) and can be applied to the design verification for other medical devices. The method can be beneficial for systems that require a large number of image datasets. This investigation covers the design, integration, image sharpness estimation, and deconvolution sharpening of the NC. The study applies this device to record two videos and extract 600 photos, including blurry and sharp images. It then uses the Laplace operator method for blur detection of the pictures. Statistics are recorded for each image's Laplace score and the distribution of clear photos in NC. In this investigation, an algorithm called t-distributed stochastic neighbor embedding (t-SNE) is introduced into the NC image sharpness estimation issue. The t-SNE is employed as a data visualization tool to determine the cluster distribution of sharp images. The result shows the t-SNE method to classify sharp images of NC is close to the method combine statistic and Laplace operator for sharp image distribution. And the image captured from the low-cost NC that applying deconvolution can sharpen the image. It shows that the NC equipment combines with low-cost hardware and software computing advantages has broader, powerful, and complex applications. For long-term purposes, it is conducive for detecting people who have Raynaud's phenomenon in rural areas of developing countries to realize the vision of health equity.

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Section: Blur Detection and Laplace Operatormentioning

confidence: 99%

Applying t-SNE to Estimate Image Sharpness of Low-cost Nailfold Capillaroscopy

Wang¹,

Chen²

2022

Intelligent Automation &Amp; Soft Computing

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“…The removal of textual elements that – in isolation – add little value to the information may occur in the following manner: Stage 2.1: Remove punctuation; Stage 2.2: Remove numbers; Stage 2.3: Remove accents; Stage 2.4: Remove special characters; Stage 2.5: Transform all words to lower case; Stage 2.6: Remove white spaces; Stage 2.7: Remove stop words; Stage 2.8: Remove custom stop words; and Stage 2.9: Synonyms. Stage 3: Stemming or Lemmatisation (Optional) : according to Soares et al (2008); Bezerra and Guimarães (2014), in this stage, the number of tokens is decreased by extracting the suffixes and prefixes that form each token, i.e. a linguistic standardisation occurs, in which the variant forms of a term are reduced to a common form called a stem. Stage 4: Frequency matrix : according to Fan et al (2006); Feldman and Sanger, 2007; Bezerra and Guimarães, 2014; Wang and Hong, 2015), this stage entails categorising stems and associating them with their respective frequency of occurrence in the texts analysed, thereby enabling inferences about their proximities, distances, synonyms and related terms.…”

Section: Theoretical Reviewmentioning

confidence: 99%

“…Stage 4: Frequency matrix : according to Fan et al (2006); Feldman and Sanger, 2007; Bezerra and Guimarães, 2014; Wang and Hong, 2015), this stage entails categorising stems and associating them with their respective frequency of occurrence in the texts analysed, thereby enabling inferences about their proximities, distances, synonyms and related terms.…”

Section: Theoretical Reviewmentioning

confidence: 99%

A model of policing strategy choice

Basílio¹,

Brum

Pereira

2020

JM2

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Purpose The purpose of this paper is to develop a method for the discovery of knowledge in emergency response databases based on police incident reports, generating information that identifies local criminal demands that allow the selection of the appropriate policing strategies portfolio to solve the problem. Design/methodology/approach The developed model uses a methodology for the discovery of knowledge involving text mining techniques using Latent Dirichlet Allocation (LDA) integrated with the ELECTRE I multicriteria method. Findings The developed method allowed the identification of the most common criminal demands that occurred from January 1 to December 31, 2016, in the policing areas studied. One of the crimes does not occur homogeneously in a particular locality. In this study, it was initially observed that 40 per cent of the crimes identified in the Integrated Public Safety Area 5, or AISP-5, (historical city center of RJ) had no correlation with AISP-19 (Copacabana - RJ), and 33 per cent of crimes crimes in AISP-19 were not identified in AISP-5. This finding guided the second part of the method that sought to identify which portfolio of policing strategies would be most appropriate for the identified demands. In this sense, using the ELECTRE I method, eight different scenarios were constructed where it can be identified that for each specific criminal demand set there is a set of policing strategies to be applied. Research limitations/implications The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics. Practical implications The developed methodology contributes in a complementary way to the identification of criminal practices and their characteristics based on reports of police occurrences stored in emergency response databases. The knowledge generated through the identification of criminal demands allows law enforcement decision makers to evaluate and choose among the available policing strategies, which best suit the reality they study, and produce the reduction of criminal indices. Social implications It is possible to infer that by choosing appropriate strategies to combat local crime, the proposed model will increase the population’s sense of safety through an effective reduction in crime. Originality/value The originality of the study lies in the integration of text mining techniques, LDA and the ELECTRE I method for detecting crime in a given location based on crime reports stored in emergency response databases, enabling identification and choice, from customized policing strategies to particular criminal demands.

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“…In recent years, with the development of science and technology, natural language processing (NLP) has been greatly developed due to its extensive application scenarios [1][2][3][4]. Text classification is an important branch of NLP.…”

Section: Introductionmentioning

confidence: 99%

Fusing Logical Relationship Information of Text in Neural Network for Text Classification

Wang

Zeng

2020

Mathematical Problems in Engineering

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With the development of computer science and information science, text classification technology has been greatly developed and its application scenarios have been widened. In traditional process of text classification, the existing method will lose much logical relationship information of text. The logical relationship information of a text refers to the relationship information among different logical parts of the text, such as title, abstract, and body. When human beings are reading, they will take title as an important part to remind the central idea of the article, abstract as a brief summary of the content of the article, and body as a detailed description of the article. In most of the text classification studies, researchers concern more about the relationship among words (word frequency, semantics, etc.) and neglect the logical relationship information of text. It will lose information about the relationship among different parts (title, body, etc.) and have an influence on the performance of text classification. Therefore, we propose a text classification algorithm—fusing the logical relationship information of text in neural network (FLRIOTINN), which complements the logical relationship information into text classification algorithms. Experiments show that the effect of FLRIOTINN is better than the conventional backpropagation neural networks which does not consider the logical relationship information of text.

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Distance Variance Score: An Efficient Feature Selection Method in Text Classification

Cited by 14 publications

References 28 publications

Applying t-SNE to Estimate Image Sharpness of Low-cost Nailfold Capillaroscopy

Applying t-SNE to Estimate Image Sharpness of Low-cost Nailfold Capillaroscopy

A model of policing strategy choice

Fusing Logical Relationship Information of Text in Neural Network for Text Classification

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