Three Conflict Methods in Multiple Classifiers that Use Dispersed Knowledge

Przybyła–Kasperek, Małgorzata

doi:10.1142/s0219622019500020

Cited by 14 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The relative distance between instances is more important than their absolute position within a given region [ 19 ]. The k -NN algorithm is suitable for analyzing large, multidimensional datasets [ 41 , 44 ], and is the optimal method when prior knowledge of the data distribution is lacking [ 17 , 45 ]. Furthermore, there is no requirement for off-line training when using the k -NN algorithm, so it is also time efficient [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Chen

Chin

2022

Diagnostics

View full text Add to dashboard Cite

Background: Deep neck infection (DNI) can lead to airway obstruction. Rather than intubation, some patients need tracheostomy to secure the airway. However, no study has used deep learning (DL) artificial intelligence (AI) to predict the need for tracheostomy in DNI patients. Thus, the purpose of this study was to develop a DL framework to predict the need for tracheostomy in DNI patients. Methods: 392 patients with DNI were enrolled in this study between August 2016 and April 2022; 80% of the patients (n = 317) were randomly assigned to a training group for model validation, and the remaining 20% (n = 75) were assigned to the test group to determine model accuracy. The k-nearest neighbor method was applied to analyze the clinical and computed tomography (CT) data of the patients. The predictions of the model with regard to the need for tracheostomy were compared with actual decisions made by clinical experts. Results: No significant differences were observed in clinical or CT parameters between the training group and test groups. The DL model yielded a prediction accuracy of 78.66% (59/75 cases). The sensitivity and specificity values were 62.50% and 80.60%, respectively. Conclusions: We demonstrated a DL framework to predict the need for tracheostomy in DNI patients based on clinical and CT data. The model has potential for clinical application; in particular, it may assist less experienced clinicians to determine whether tracheostomy is necessary in cases of DNI.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Chen

Chin

2022

Diagnostics

View full text Add to dashboard Cite

show abstract

“…Thanks to this, we obtain diversified classifiers for dispersed data (as local tables are independent), which, as we know from the literature [ 13 ], is important for an ensemble of classifiers. In addition, the k -nearest neighbors method has already been used in previous studies for dispersed data [ 14 , 15 ] and has produced good results. To generate the final global decision, we propose training a neural network in making the final decision using the probability vectors generated based on local tables.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Pawlak’s conflict analysis model [ 28 , 29 ] is yet another approach to conflict recognition that provides excellent solutions in a variety of applications [ 30 , 31 ]. Pawlak conflict analysis model was also considered in the context of dispersed data in the papers [ 32 , 33 , 34 ]. This application shows that the Pawlak model provides excellent results for dispersed data when tables are aggregated within coalitions.…”

Section: Introductionmentioning

confidence: 99%

“…Here, the compatibility of tables is examined in terms of the information stored in them—the values on the attributes. In contrast, the papers [ 32 , 33 , 34 ] consider compatibility in terms of predictions generated by the base models created based on the tables. Another difference is that in this paper we assume that in local tables the same attributes are present, while in the papers [ 32 , 33 , 34 ] there was no such assumption.…”

Section: Introductionmentioning

confidence: 99%

New Classification Method for Independent Data Sources Using Pawlak Conflict Model and Decision Trees

Przybyła–Kasperek

Kusztal

2022

Entropy

Self Cite

View full text Add to dashboard Cite

The research concerns data collected in independent sets—more specifically, in local decision tables. A possible approach to managing these data is to build local classifiers based on each table individually. In the literature, many approaches toward combining the final prediction results of independent classifiers can be found, but insufficient efforts have been made on the study of tables’ cooperation and coalitions’ formation. The importance of such an approach was expected on two levels. First, the impact on the quality of classification—the ability to build combined classifiers for coalitions of tables should allow for the learning of more generalized concepts. In turn, this should have an impact on the quality of classification of new objects. Second, combining tables into coalitions will result in reduced computational complexity—a reduced number of classifiers will be built. The paper proposes a new method for creating coalitions of local tables and generating an aggregated classifier for each coalition. Coalitions are generated by determining certain characteristics of attribute values occurring in local tables and applying the Pawlak conflict analysis model. In the study, the classification and regression trees with Gini index are built based on the aggregated table for one coalition. The system bears a hierarchical structure, as in the next stage the decisions generated by the classifiers for coalitions are aggregated using majority voting. The classification quality of the proposed system was compared with an approach that does not use local data cooperation and coalition creation. The structure of the system is parallel and decision trees are built independently for local tables. In the paper, it was shown that the proposed approach provides a significant improvement in classification quality and execution time. The Wilcoxon test confirmed that differences in accuracy rate of the results obtained for the proposed method and results obtained without coalitions are significant, with a p level = 0.005. The average accuracy rate values obtained for the proposed approach and the approach without coalitions are, respectively: 0.847 and 0.812; so the difference is quite large. Moreover, the algorithm implementing the proposed approach performed up to 21-times faster than the algorithm implementing the approach without using coalitions.

show abstract

Three Conflict Methods in Multiple Classifiers that Use Dispersed Knowledge

Cited by 14 publications

References 35 publications

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

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

New Classification Method for Independent Data Sources Using Pawlak Conflict Model and Decision Trees

Contact Info

Product

Resources

About