Genetic programming based Choquet integral for multi-source fusion

Simth, Ryan E.; Anderson, Derek T.; Zare, Alina; Ball, John; Smock, Brandon; Fairley, Josh R.; Howington, Stacy E.

doi:10.1109/fuzz-ieee.2017.8015481

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…The utility of the ChI has been demonstrated in numerous applications, e.g., [4,6,21,24]. The ChI is a powerful, nonlinear aggregation operator that is parameterized by a fuzzy measure (FM).…”

Section: Choquet Integralmentioning

confidence: 99%

Information Fusion-2-Text: Explainable Aggregation via Linguistic Protoforms

Murray

Anderson

Havens

et al. 2020

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Recent advancements and applications in artificial intelligence (AI) and machine learning (ML) have highlighted the need for explainable, interpretable, and actionable AI-ML. Most work is focused on explaining deep artificial neural networks, e.g., visual and image captioning. In recent work, we established a set of indices and processes for explainable AI (XAI) relative to information fusion. While informative, the result is information overload and domain expertise is required to understand the results. Herein, we explore the extraction of a reduced set of higher-level linguistic summaries to inform and improve communication with non-fusion experts. Our contribution is a proposed structure of a fusion summary and method to extract this information from a given set of indices. In order to demonstrate the usefulness of the proposed methodology, we provide a case study for using the fuzzy integral to combine a heterogeneous set of deep learners in remote sensing for object detection and land cover classification. This case study shows the potential of our approach to inform users about important trends and anomalies in the models, data and fusion results. This information is critical with respect to transparency, trustworthiness, and identifying limitations of fusion techniques, which may motivate future research and innovation.

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Section: Choquet Integralmentioning

confidence: 99%

Information Fusion-2-Text: Explainable Aggregation via Linguistic Protoforms

Murray

Anderson

Havens

et al. 2020

Information Processing and Management of Uncertainty in Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

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“…The fuzzy integral has been demonstrated numerous times in a variety of applications; e.g., explosive hazard detection [22,23], computer vision [24], pattern recognition [25,26,27], multi-criteria decision making [28,29], forensic anthropology [30,31], fuzzy logic [32], multiple kernel learning [33], multiple instance learning [34], ontologies [35], missing data [36], and deep learning for remote sensing [37,38], to name a few. The ChI is a nonlinear aggregation function parameterized by the FM.…”

Section: Measure and Choquet Integralmentioning

confidence: 99%

“…Namely, we focus on the fuzzy integral (FI). The FI is selected because it has been successfully applied across a variety of applications like computer vision [24], multi-criteria decision making [28,29], multi-sensor fusion [22], remote sensing [53], neural networks [54], etc. The FI is a parametric function that yields a wide class of operators used in practice; the max, min, linear order statistics, and more.…”

Section: Chapter Explainable Ai For the Choquet Integralmentioning

confidence: 99%

“…The utility of the ChI has been demonstrated in numerous applications, e.g., [24,28,22,53]. The ChI is a powerful, nonlinear aggregation operator that is parameterized by a fuzzy measure (FM).…”

Section: Choquet Integralmentioning

confidence: 99%

“…The ChI is an effective mechanism to aggregate data in many applications including explosive hazard detection [22,23], pattern recognition [25,27], multi-criteria decision making [28,29], fuzzy logic [32], and deep learning for remote sensing [37]. The Herein, we focus on how to transfer learn variables from another domain, e.g., a previously known model.…”

Section: Introductionmentioning

confidence: 99%

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Explainable data fusion

Murray¹

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The recent resurgence of Artificial Intelligence (AI), specifically in the context of applications like healthcare, security and defense, IoT, and other areas that have a big impact on human life, has led to a demand for eXplainable AI (XAI). The production of explanations is argued to be a key aspect of achieving goals like trustworthiness and transparent versus opaque AI. XAI is also of fundamental academic interest with respect to helping us identifying weaknesses in the pursuit of making better AI. Herein, I focus on one piece of the AI puzzle, information fusion. In this work, I propose XAI fusion indices, linguistic summaries (aka textual explanations) of these indices, and local explanations for the fuzzy integral. However, a limitation of these indices is its tailored to highly educated fusion experts, and it is not clear what to do with these explanations. Herein, I extend the introduced indices to actionable explanations, which are demonstrated in the context of two case studies; multi-source fusion and deep learning for remote sensing. This work ultimately shows what XAI for fusion is and how to create actionable insights.

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