Clustering with Minimum Spanning Tree using TOPSIS with Multi-Criteria Information

Esgario, José G. M.; Krohling, Renato A.

doi:10.1109/fuzz-ieee.2018.8491554

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…A sensitivity analysis was performed to determine the weights of the evaluation criteria used in TOPSIS [9]. The analysis consisted in the execution of the algorithm using the Accuracy over the Training Dataset and the Number of Rules of the FRBCS as TOPSIS criteria for the 10 datasets and for vectors of weight in intervals of 0.05.…”

Section: Methodsmentioning

confidence: 99%

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Generating a Fuzzy Rule Based Classification System by genetic learning of granularity level using TOPSIS

Araujo

Krohling

2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Fuzzy Rule Based Classification Systems (FRBCSs) are widely used tools in classification problems. An important aspect in the design of a FRBCS is the number of fuzzy labels per variable (granularity level), which significantly influences the performance of the fuzzy system. Another relevant issue to be considered when generating a FRBCS is the accuracy-interpretability tradeoff, which can be addressed in the context of multiobjective optimization. Thus, in this work, we propose a new approach to design a FR-BCS in which the accuracy and the interpretability (number of rules) of the FRBCS are considered objectives to be treated with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We applied our method to several well-known standard classification datasets and the results show the feasibility of the proposed approach.

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Section: Methodsmentioning

confidence: 99%

“…Since the criteria have different origins, it is necessary to normalize the decision matrix D so that it is possible to compare the values relative to different criteria considered [9]. In this work, the decision matrix D is normalized for each criterion according to…”

Section: Topsismentioning

confidence: 99%

Generating a Fuzzy Rule Based Classification System by genetic learning of granularity level using TOPSIS

Araujo

Krohling

2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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“…There have been many clustering algorithms, such as k-means (KM) and its variants [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Others are based on minimal spanning trees [ 17 , 18 , 19 ], density analysis [ 20 , 21 , 22 , 23 , 24 , 25 ], spectral analysis [ 26 , 27 ], subspace clustering [ 28 , 29 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Kernel Probabilistic K-Means Clustering

Liu

Zhang

et al. 2021

Sensors

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Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian optimization. To solve this problem, an equivalent model, called kernel probabilistic k-means (KPKM), is proposed here. The novel model relates KFCM to kernel k-means (KKM) in a unified mathematic framework. Moreover, the proposed KPKM can be addressed by the active gradient projection (AGP) method, which is a nonlinear programming technique with constraints of linear equalities and linear inequalities. To accelerate the AGP method, a fast AGP (FAGP) algorithm was designed. The proposed FAGP uses a maximum-step strategy to estimate the step length, and uses an iterative method to update the projection matrix. Experiments demonstrated the effectiveness of the proposed method through a performance comparison of KPKM with KFCM, KKM, FCM and k-means. Experiments showed that the proposed KPKM is able to find nonlinearly separable structures in synthetic datasets. Ten real UCI datasets were used in this study, and KPKM had better clustering performance on at least six datsets. The proposed fast AGP requires less running time than the original AGP, and it reduced running time by 76–95% on real datasets.

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A Multiobjective Genetic Approach with TOPSIS to Learn Fuzzy Rule-Based Classification Systems

Araujo

Krohling

2019

2019 8th Brazilian Conference on Intelligent Systems (BRACIS)

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Clustering with Minimum Spanning Tree using TOPSIS with Multi-Criteria Information

Cited by 3 publications

References 14 publications

Generating a Fuzzy Rule Based Classification System by genetic learning of granularity level using TOPSIS

Generating a Fuzzy Rule Based Classification System by genetic learning of granularity level using TOPSIS

Kernel Probabilistic K-Means Clustering

A Multiobjective Genetic Approach with TOPSIS to Learn Fuzzy Rule-Based Classification Systems

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