Measures of the Shapley index for learning lower complexity fuzzy integrals

Pinar, Anthony J.; Anderson, Derek T.; Havens, Timothy C.; Zare, Alina; Adeyeba, Titilope

doi:10.1007/s41066-017-0045-6

Cited by 25 publications

(9 citation statements)

References 39 publications

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“…To answer this question, a method based on the computation of the Shapley Index [31] is proposed in this study. The Shapley index is generally used to assess the quality of the feature discrimination for the resulting models.…”

Section: Probabilistic and Possibilistic Modeling Of The Information mentioning

confidence: 99%

An intelligent quality-based approach to fusing multi-source possibilistic information

et al. 2020

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Recently, Yager and Petry were proposing a quality-based methodology to combine data provided by multiple probabilistic sources to improve the quality of information for decision-makers. This paper offers a sort of companion paper that adapts this methodology to possibilistic sources. Possibility theory is particularly well suited to cope with incomplete information from poor-data sources. The methodology and algorithms used for the probabilistic approach are adapted for the possibilistic case. Both approaches are then compared by the means of a numerical example and four experimental benchmark datasets: one, the IRIS data set, being data-poorer than the three other ones (Diabetes dataset, Glass dataset and Liver-disorder dataset). A vector representation is introduced for a possibility distribution as in the probabilistic case and, the Gini's formulation of entropy is being used. However, the Gini's entropy has to be used differently than with the probabilistic case. This has an impact on the selection of subsets. A fusion scheme is designed to select the best-quality subsets according to two information quality factors: quantity of information and source credibility. Results obtained from comparison of both approaches on the four experimental benchmarks confirm the superiority of the possibilistic approach in the presence of information scarcity or incompleteness.

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Section: Probabilistic and Possibilistic Modeling Of The Information mentioning

confidence: 99%

An intelligent quality-based approach to fusing multi-source possibilistic information

et al. 2020

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“…Another approach to obtain ensemble classification is the use of the Fuzzy Integral (FI) aggregation defined with respect to a Fuzzy Measure (FM) [4][5][6][7][8]. The FI is a non-linear aggregation operator to fuse weighted information from multiple sources, where the weights are captured by a FM.…”

Section: Introductionmentioning

confidence: 99%

“…These works further concluded that DeFIMKL was the best among decision level fusion based FI-FM based ensemble classifiers and thus it has been selected in the work as representative of the FI-FM based ensemble classifier family. However, no in-depth comparison of the FI-FM based ensemble classifier with other ensemble methods have been found in the literature [4][5][6][7][8][9] (discussed in detail in section 2.3). Thus, the motivation of this study is to determine the performance of FM-FI based ensemble methods for the purpose of ensemble classification.…”

Section: Introductionmentioning

confidence: 99%

Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications

Medina¹

2018

Communications in Computer and Information Science

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The Fuzzy Integral (FI) is a non-linear aggregation operator which enables the fusion of information from multiple sources in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. Based on the expected potential of non-linear aggregation offered by the FI, its application to decision-level fusion in ensemble classifiers, i.e. to fuse multiple classifiers outputs towards one superior decision level output, has recently been explored. A key example of such a FI-FM ensemble classification method is the Decision-level Fuzzy Integral Multiple Kernel Learning (DeFIMKL) algorithm, which aggregates the outputs of kernel based classifiers through the use of the Choquet FI with respect to a FM learned through a regularised quadratic programming approach. While the approach has been validated against a number of classifiers based on multiple kernel learning, it has thus far not been compared to the state-of-the-art in ensemble classification. Thus, this paper puts forward a detailed comparison of FI-FM based ensemble methods, specifically the DeFIMKL algorithm, with state-of-the art ensemble methods including Adaboost, Bagging, Random Forest and Majority Voting over 20 public datasets from the UCI machine learning repository. The results on the selected datasets suggest that the FI based ensemble classifier performs both well and efficiently, indicating that it is a viable alternative when selecting ensemble classifiers and indicating that the non-linear fusion of decision level outputs offered by the FI provides expected potential and warrants further study.

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“…Because of the inherent complexity and uncertainty of the decision situation or the existence of multiple and conflicting objectives, decision-making problems are complex and difficult; particularly in the era of big data, decision making becomes more complicated because the huge amounts of decision information and alternatives are continuously growing. Many new decision-making methods, such as granular computing techniques [1,[6][7][8][9][10], have been proposed for expressing complex or uncertain information in decision-making processes and solving decision-making problems [11][12][13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A New Hesitant Fuzzy Linguistic TOPSIS Method for Group Multi-Criteria Linguistic Decision Making

2017

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Hesitant fuzzy linguistic decision making is a focus point in linguistic decision making, in which the main method is based on preference ordering. This paper develops a new hesitant fuzzy linguistic TOPSIS method for group multi-criteria linguistic decision making; the method is inspired by the TOPSIS method and the preference degree between two hesitant fuzzy linguistic term sets (HFLTSs). To this end, we first use the preference degree to define a pseudo-distance between two HFLTSs and analyze its properties. Then we present the positive (optimistic) and negative (pessimistic) information of each criterion provided by each decision maker and aggregate these by using weights of decision makers to obtain the hesitant fuzzy linguistic positive and negative ideal solutions. On the basis of the proposed pseudo-distance, we finally obtain the positive (negative) ideal separation matrix and a new relative closeness degree to rank alternatives. We also design an algorithm based on the provided method to carry out hesitant fuzzy linguistic decision making. An illustrative example shows the elaboration of the proposed method and comparison with the symbolic aggregation-based method, the hesitant fuzzy linguistic TOPSIS method and the hesitant fuzzy linguistic VIKOR method; it seems that the proposed method is a useful and alternative decision-making method.

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Measures of the Shapley index for learning lower complexity fuzzy integrals

Cited by 25 publications

References 39 publications

An intelligent quality-based approach to fusing multi-source possibilistic information

An intelligent quality-based approach to fusing multi-source possibilistic information

Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications

A New Hesitant Fuzzy Linguistic TOPSIS Method for Group Multi-Criteria Linguistic Decision Making

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