Interval type-2 outlier-robust picture fuzzy clustering and its application in medical image segmentation

Wang, Yingxu; Wei, Huiqin; Zhou, Jin; Li, Tianjun; Chen, C.L. Philip

doi:10.1016/j.asoc.2022.108891

Cited by 19 publications

(4 citation statements)

References 29 publications

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“…The square bracket part represents the data site, and the numbers in the square bracket represent the feature value of the data point. For example, {[0, 1, 2], [1,2,3]} represents that there are two feature subsets in the horizontal distribution, the first of which consists of attributes 0, 1, and 2, and the second of which includes attributes 1, 2, and 3. Table 4 shows the optimal value of each algorithm's parameter in each dataset, and its value range is (1,5].…”

Section: Experimental Preparationmentioning

confidence: 99%

“…The total number of Samples (30) [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]} P2{[0, 2,4,6,8,10,12,14,16,18,20,22,24,26,28], [1,3,5,7,9,11,13,15,17,19,21,23,25,27,29]} P3{[0, 1,2,3,4,5,6,7,8,…”

Section: All Predictions Correctly Number Of Samplesmentioning

confidence: 99%

“…If objects in the same clusters are more similar, and ones in different clusters are more dissimilar, the final clustering performance will be better. At present, clustering has been widely used in many fields [1][2][3][4][5][6][7] such as data mining, pattern recognition, image segmentation, fuzzy network, bioinformatics, etc. In order to make clustering widely available in more fields, it can be applied to large-scale group decision-making [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Collaborative possibilistic fuzzy clustering based on information bottleneck

Chen

Liu

2023

IFS

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In fuzzy clustering algorithms, the possibilistic fuzzy clustering algorithm has been widely used in many fields. However, the traditional Euclidean distance cannot measure the similarity between samples well in high-dimensional data. Moreover, if there is an overlap between clusters or a strong correlation between features, clustering accuracy will be easily affected. To overcome the above problems, a collaborative possibilistic fuzzy clustering algorithm based on information bottleneck is proposed in this paper. This algorithm retains the advantages of the original algorithm, on the one hand, using mutual information loss as the similarity measure instead of Euclidean distance, which is conducive to reducing subjective errors caused by arbitrary choices of similarity measures and improving the clustering accuracy; on the other hand, the collaborative idea is introduced into the possibilistic fuzzy clustering based on information bottleneck, which can form an accurate and complete representation of the data organization structure based on make full use of the correlation between different feature subsets for collaborative clustering. To examine the clustering performance of this algorithm, five algorithms were selected for comparison experiments on several datasets. Experimental results show that the proposed algorithm outperforms the comparison algorithms in terms of clustering accuracy and collaborative validity.

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Section: Experimental Preparationmentioning

confidence: 99%

Section: All Predictions Correctly Number Of Samplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Collaborative possibilistic fuzzy clustering based on information bottleneck

Chen

Liu

2023

IFS

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“…Furthermore, in the last three years of studies on higher-order types of FLS in particular, the designed and developed applications of interval type-2 fuzzy logic have increased significantly [48][49][50][51][52][53][54]. These type-2-based FLS applications have been identified in artificial intelligence (AI) [55][56][57][58][59], adaptive control [60][61][62][63][64][65][66], electric motor control [67][68][69][70][71][72], Internet of Things (IoT) [73][74][75][76][77], digital image processing [78][79][80][81][82][83][84] and other areas [85][86][87]. Of course, the application of interval type-2 fuzzy logic in the domain of control has recently attracted a lot of attention due to its better performance under uncertain conditions.…”

Section: Number Of Output Fuzzy Membership Functionsmentioning

confidence: 99%

Real-Time Metaheuristic Algorithm for Dynamic Fuzzification, De-Fuzzification and Fuzzy Reasoning Processes

2022

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This paper presents a systematic approach to designing a dynamic metaheuristic fuzzy logic controller (FLC) to control a piece of non-linear plant. The developed controller is a multiple-input–multiple-output (MIMO) system. However, with the proposed control mechanism is possible to adapt it to single-input–single-output (SISO) systems as well. During real-time operation, the dynamic behavior of the proposed fuzzy controller is influenced by a metaheuristic particle swarm optimization (PSO) mechanism. Nevertheless, to analyze the performance of the developed dynamic metaheuristic FLC as a piece of non-linear plant, a 1 kW four-wheel independent-drive electric rover is controlled under different road constraints. The test results show that the proposed dynamic metaheuristic FLC maintains the wheel slip ratio of all four wheels to less than 0.35 and a top recorded translational speed of 90 km/h is maintained for a fixed orientation.

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