Linguistic description of adult skeletal age-at-death estimations from fuzzy integral acquired fuzzy sets

Anderson, Derek T.; Keller, James M.; Anderson, Melissa F.; Wescott, Daniel J.

doi:10.1109/fuzzy.2011.6007421

Cited by 9 publications

(39 citation statements)

References 41 publications

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“…Recently, we extended the case of FN-valued integrands to convex, non-normal FS-valued integrands, enabling fusion in real world contexts such as forensic applications in anthropology and criminal justice where non-normal FS inputs are a reality [12][13][14]. Table II is an overview of key papers in the literature, a non-exhaustive list, that is focused on extending the FI and FM.…”

Section: Motivationmentioning

confidence: 99%

“…ith source (x i ∈ X) [13,14] to fuse complicated non-convex FS-valued data. Additional examples of applications where this applies range from agreement-based modeling of survey data [15] to explosive hazard detection (e.g., [6]), computing with respect to linguistic descriptions of spatial relations of sensor data generated from the histograms-of-forces (e.g.. [16][17][18]), and even sensor measurement-based histograms [19].…”

Section: Motivationmentioning

confidence: 99%

“…However, a few works have appeared regarding the generalization of both the integrand as well as the FM with respect to intervals and Fuzzy Sets (FSs) [2,[4][5][6][7][8][9][10][11] or more precisely, Fuzzy Numbers (FNs), i.e., convex and normal type-1 FSs. In [12] and [13,14], Anderson et al introduced indirect and direct extensions of the FI for the case of convex non-normal FS-valued integrands.…”

Section: Introductionmentioning

confidence: 99%

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Generalization of the Fuzzy Integral for discontinuous interval- and non-convex interval fuzzy set-valued inputs

Wagner

Anderson

Havens

2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-The Fuzzy Integral (FI) is a powerful approach for non-linear data aggregation. It has been used in many settings to combine evidence (typically objective) with the known "worth" (typically subjective) of each data source, where the latter is encoded in a Fuzzy Measure (FM). While initially developed for the case of numeric evidence (integrand) and numeric FM, Grabisch et al. extended the FI to the cases of continuous intervals and normal, convex fuzzy sets (i.e., fuzzy numbers). However, in many real-world applications, e.g., explosive hazard detection based on multi-sensor and/or multi-feature fusion, agreement based modeling of survey data, anthropology and forensic science, or computing with respect to linguistic descriptions of spatial relations from sensor data, discontinuous interval and/or nonconvex fuzzy set data may arise. The problem is no theory and algorithm currently exists for calculating the FI for such a case. Herein, we propose an extension of the FI to discontinuous interval-and convex normal Interval Fuzzy Set (IFS)-valued integrands (with a numeric FM). Our approach arises naturally from analysis of the Extension Principle. Further, we provide a computationally efficient approach to computing the proposed extension based on the union of the FIs on the combinations of continuous sub-intervals and we demonstrate the approach using examples for both the Choquet FI (CFI) and Sugeno FI (SFI).

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generalization of the Fuzzy Integral for discontinuous interval- and non-convex interval fuzzy set-valued inputs

Wagner

Anderson

Havens

2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…An interesting piece of work on CWW can be found in [183] where the author defined CWW as a symbolic generalization of the fuzzy logic. In the recent years, several papers have been published that utilizes the concept of linguistic summarization in the fuzzy system that are successfully applied in the real world applications [104,184,185,186,187,188]. In these works, a complete sentence instead of numerical data or a crisp answer in a conventional decision making systems is preferable as an output; for example, "the resident has fallen in the living room and is down for a long time".…”

Section: Linguistic Supportmentioning

confidence: 99%

Fuzzy human motion analysis: A review

Lim

Vats

Chan

2015

Pattern Recognition

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Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on. However, most of these real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, the fuzzy set theory has been applied and showed great success in the recent past. In this paper, we aim at reviewing the fuzzy set oriented approaches for HMA, individuating how the fuzzy set may improve the HMA, envisaging and delineating the future perspectives. To the best of our knowledge, there is not found a single survey in the current literature that has discussed and reviewed fuzzy approaches towards the HMA. For ease of understanding, we conceptually classify the human motion into three broad levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA.

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“…Initially, the gFI was created for skeletal age-at-death estimation in forensic anthropology [15,16]. Due to space considerations herein, see [9] for full mathematical detail, proofs and in-depth analysis.…”

Section: Generalized Fuzzy Integralmentioning

confidence: 99%

Fuzzy Integral for Rule Aggregation in Fuzzy Inference Systems

Tomlin

Anderson

Wagner

et al. 2016

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Abstract. The fuzzy inference system (FIS) has been tuned and revamped many times over and applied to numerous domains. New and improved techniques have been presented for fuzzification, implication, rule composition and defuzzification, leaving one key component relatively underrepresented, rule aggregation. Current FIS aggregation operators are relatively simple and have remained more-or-less unchanged over the years. For many problems, these simple aggregation operators produce intuitive, useful and meaningful results. However, there exists a wide class of problems for which quality aggregation requires nonadditivity and exploitation of interactions between rules. Herein, we show how the fuzzy integral, a parametric non-linear aggregation operator, can be used to fill this gap. Specifically, recent advancements in extensions of the fuzzy integral to "unrestricted" fuzzy sets, i.e., subnormal and nonconvex, makes this now possible. We explore the role of two extensions, the gFI and the NDFI, discuss when and where to apply these aggregations, and present efficient algorithms to approximate their solutions.

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Linguistic description of adult skeletal age-at-death estimations from fuzzy integral acquired fuzzy sets

Cited by 9 publications

References 41 publications

Generalization of the Fuzzy Integral for discontinuous interval- and non-convex interval fuzzy set-valued inputs

Generalization of the Fuzzy Integral for discontinuous interval- and non-convex interval fuzzy set-valued inputs

Fuzzy human motion analysis: A review

Fuzzy Integral for Rule Aggregation in Fuzzy Inference Systems

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