Clustered Bayesian classification for within-class separation

Sağlam, Fatih; Yıldırım, Emre; Cengiz, Mehmet Ali

doi:10.1016/j.eswa.2022.118152

Cited by 4 publications

(2 citation statements)

References 68 publications

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“…This expression can be rewritten using Bayesian formulas as: Plain Bayesian classification is performed under the simple assumption that the attribute values are also independent of each other when explicitly given target values [20][21]. In other words, the assumption also suggests that in a given instance target value situation, this situation is observable given the joint 12 , , , n a a a K probability is in fact equal to the product of the probabilities of the individual independent attributes:…”

Section: Plain Bayesian Classificationmentioning

confidence: 99%

Design of cultural and creative experience products in scenic spots based on CORE model

Dai,

Zhang

2023

Applied Mathematics and Nonlinear Sciences

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Under the background of “cultural and tourism integration”, scenic cultural and creative experience products are an important means to enhance the communication power of scenic spots and generate income. In this paper, the CORE model is used as the design concept, and the CORE design process for scenic cultural and creative products is constructed. The plain Bayesian classification algorithm is used to extract review feedback from the product, and the vectorization of the extracted reviews is achieved using the FastText model. The vectorized reviews are input into the logistic regression classification model to obtain sentiment classification results. A fused sentiment feature vector is constructed by combining Apirior and a sentiment dictionary aimed at separating sentiment vector classification. Finally, the experience effect of cultural and creative products is explored as an example of X scenic spot, and the optimization strategy of product design is explored. The attribute feature of X Scenic Spot’s cultural and creative experience product, “color” embraces four emotional attributes, and the corresponding adaptations are 4.77, 5.42, 5.40, and 5.38, respectively. The SDs of the emotional attributes of the existing product are The SDs of the emotional attributes of the existing products are 1.141, 1.025, and 0.184, respectively, and the differences between the product descriptions and the users’ opinions on the perceptual attributes are large, which provides the evaluation correlation in the CORE model. Under the guidance of the CORE model, four aspects of cultural and creative marketing, scenic area IP, consumer needs and product experience dimensions are proposed.

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Section: Plain Bayesian Classificationmentioning

confidence: 99%

Design of cultural and creative experience products in scenic spots based on CORE model

Dai,

Zhang

2023

Applied Mathematics and Nonlinear Sciences

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“…Sun et al 13 construct a multi-label classification method based on neighborhood information and it is used for incomplete data with missing labels in neighborhood decision-making system. Sauglam et al 14 introduce a new clustering Bayesian classification method to detect different concentrations in a class. To reduce the uncertainty introduced by the noise data in classification problem, Yao et al 15 propose a new hybrid integrated credit scoring model based on stacked noise detection and weight assignment to remove or adjust the noise data in the original data set and form the noise detection training data.…”

Section: Introductionmentioning

confidence: 99%

A new correlation belief function in Dempster-Shafer evidence theory and its application in classification

Tang

Zhang

Zhou

et al. 2023

Sci Rep

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Uncertain information processing is a key problem in classification. Dempster-Shafer evidence theory (D-S evidence theory) is widely used in uncertain information modelling and fusion. For uncertain information fusion, the Dempster’s combination rule in D-S evidence theory has limitation in some cases that it may cause counterintuitive fusion results. In this paper, a new correlation belief function is proposed to address this problem. The proposed method transfers the belief from a certain proposition to other related propositions to avoid the loss of information while doing information fusion, which can effectively solve the problem of conflict management in D-S evidence theory. The experimental results of classification on the UCI dataset show that the proposed method not only assigns a higher belief to the correct propositions than other methods, but also expresses the conflict among the data apparently. The robustness and superiority of the proposed method in classification are verified through experiments on different datasets with varying proportion of training set.

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Development and application of an optimised Bayesian shrinkage prior for spectroscopic biomedical diagnostics

Chu,

Buchan,

Smith

et al. 2024

Computer Methods and Programs in Biomedicine

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Clustered Bayesian classification for within-class separation

Cited by 4 publications

References 68 publications

Design of cultural and creative experience products in scenic spots based on CORE model

Design of cultural and creative experience products in scenic spots based on CORE model

A new correlation belief function in Dempster-Shafer evidence theory and its application in classification

Development and application of an optimised Bayesian shrinkage prior for spectroscopic biomedical diagnostics

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