Dispersed decision-making system with fusion methods from the rank level and the measurement level – A comparative study

Przybyła–Kasperek, Małgorzata; Wakulicz-Deja, Alicja

doi:10.1016/j.is.2017.05.002

Cited by 18 publications

(6 citation statements)

References 28 publications

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“…The set of classes that have the maximum value of similarity is the final decision. In this article, the Hamming distance is used, because based on previous experience [ 22 ] it produces the best results.…”

Section: Methodsmentioning

confidence: 99%

Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

Przybyła–Kasperek

Marfo

2021

Entropy

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The article concerns the problem of classification based on independent data sets—local decision tables. The aim of the paper is to propose a classification model for dispersed data using a modified k-nearest neighbors algorithm and a neural network. A neural network, more specifically a multilayer perceptron, is used to combine the prediction results obtained based on local tables. Prediction results are stored in the measurement level and generated using a modified k-nearest neighbors algorithm. The task of neural networks is to combine these results and provide a common prediction. In the article various structures of neural networks (different number of neurons in the hidden layer) are studied and the results are compared with the results generated by other fusion methods, such as the majority voting, the Borda count method, the sum rule, the method that is based on decision templates and the method that is based on theory of evidence. Based on the obtained results, it was found that the neural network always generates unambiguous decisions, which is a great advantage as most of the other fusion methods generate ties. Moreover, if only unambiguous results were considered, the use of a neural network gives much better results than other fusion methods. If we allow ambiguity, some fusion methods are slightly better, but it is the result of this fact that it is possible to generate few decisions for the test object.

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Section: Methodsmentioning

confidence: 99%

Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

Przybyła–Kasperek

Marfo

2021

Entropy

Self Cite

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“…In addition to the evidence theory used in this paper, there exist many other fusion methods. Przybyła et al introduced four fusion methods from the rank level and nine from the measurement level and compared their experimental results [35]. They concluded that the Borda count method is the best method for producing definite results, followed by the sum rule and the product rule.…”

Section: B Collective Preference After Fusionmentioning

confidence: 99%

Study on the Location of Private Clinics Based on K-Means Clustering Method and an Integrated Evaluation Model

Wang

Shao

et al. 2020

IEEE Access

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Appropriate location is an important prerequisite for the long-term survival and development of private medical institutions. However, in both theory and practice, the issue of location decision-making for private clinics has not been fully studied. We therefore aimed to provide a feasible scheme for the location of new private clinics. This paper combines the k-means clustering method and an integrated 2DULVs (twodimensional uncertain language variables)-TOPSIS (technique for order preference by similarity to ideal solution)-DSCCR (Dempster-Shafer conjunctive combination rule) model to screen and evaluate all of the areas in the target region for an Internet medical company to set up offline clinics. We first created geographic grids using GIS and collected point of interest (POI) data. We then used the k-means clustering method to obtain 10 suitable grids as alternatives. Last, we established an evaluation index system and used the proposed model to rank them. The results show that grids 178, 179 and 202 are more suitable for the company to establish offline clinics in the expansion of business. The results of this study are also consistent with those of the other three fusion methods. This paper provides a beneficial attempt for private clinics to make location decisions and can be extended to the strategic decision-making of other industries or other issues.

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“…• ψ SM -outputs of the linear classifiers were normalized using the softmax rule, and then combined according to (10).…”

Section: Design Of the Experimentsmentioning

confidence: 99%

“…The combining phase can be achieved using different types of the classifier's output: a class label [6], a subset of labels ordered by plausibility [10], a vector of all possible labels with corresponding scoring functions [11]. The scoring function may have a different nature depending on the type of individual learners.…”

Section: Introductionmentioning

confidence: 99%

Linear Classifier Combination via Multiple Potential Functions

Trajdos,

Burduk

2020

Preprint

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A vital aspect of the classification based model construction process is the calibration of the scoring function. One of the weaknesses of the calibration process is that it does not take into account the information about the relative positions of the recognized objects in the feature space. To alleviate this limitation, in this paper, we propose a novel concept of calculating a scoring function based on the distance of the object from the decision boundary and its distance to the class centroid. An important property is that the proposed score function has the same nature for all linear base classifiers, which means that outputs of these classifiers are equally represented and have the same meaning. The proposed approach is compared with other ensemble algorithms and experiments on multiple Keel datasets demonstrate the effectiveness of our method. To discuss the results of our experiments, we use multiple classification performance measures and statistical analysis.

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Dispersed decision-making system with fusion methods from the rank level and the measurement level – A comparative study

Cited by 18 publications

References 28 publications

Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

Study on the Location of Private Clinics Based on K-Means Clustering Method and an Integrated Evaluation Model

Linear Classifier Combination via Multiple Potential Functions

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