Integration of neural networks with knowledge-based systems

Ultsch, Alfred; Korus, Dieter

doi:10.1109/icnn.1995.488899

Cited by 10 publications

(9 citation statements)

References 7 publications

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“…There are two approaches for the explanation of machine learning systems: prediction, interpretation, and justification, which is used to explain sub-symbolic ML systems (see [14]), and intrinsic interpretable approaches called symbolic ML systems (see [15]), which are explained through reasoning [16]. Recently sub-symbolic ML systems were introduced in [17,18] for which interpretation and justification can be performed with local interpretable model-agnostic explanation (LIME) [19] or its generalization SHapley Additive exPlanations (SHAP) [20].…”

Section: Related Workmentioning

confidence: 99%

Explainable AI Framework for Multivariate Hydrochemical Time Series

Thrun

Ultsch

2021

MAKE

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The understanding of water quality and its underlying processes is important for the protection of aquatic environments. With the rare opportunity of access to a domain expert, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series. The XAI provides explanations that are interpretable by domain experts. In three steps, it combines a data-driven choice of a distance measure with supervised decision trees guided by projection-based clustering. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The framework, called DDS-XAI, does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two state of the art XAIs called eUD3.5 and iterative mistake minimization (IMM) were unable to provide meaningful and relevant explanations from the three multivariate time series data. The DDS-XAI framework can be swiftly applied to new data. Open-source code in R for all steps of the XAI framework is provided and the steps are structured application-oriented.

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Section: Related Workmentioning

confidence: 99%

Explainable AI Framework for Multivariate Hydrochemical Time Series

Thrun

Ultsch

2021

MAKE

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“…There are two approaches for the explanation of machine learning systems: prediction, interpretation, and justification that is used for sub symbolic ML systems (defined in [13]) and interpretable approaches for symbolic ML systems (defined in [14]), which are explained through reasoning [15]. For the former, a wellknown example is LIME [16], which approximates any classifier or regressor locally with an interpretable model.…”

Section: Related Workmentioning

confidence: 99%

Explainable AI Framework for Multivariate Hydrochemical Time Series

Thrun¹,

Ultsch²

2020

Preprint

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The understanding of water quality and its underlying processes is important for the protection of aquatic environments enabling the rare opportunity of access to a domain expert. Hence, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series resulting in explanations that are interpretable by a domain expert. The XAI combines in three steps a data-driven choice of a distance measure with explainable cluster analysis through supervised decision trees. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The XAI does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two comparable decision-based XAIs were unable to provide meaningful and relevant explanations from the multivariate time series data. Open-source code in R for the three steps of the XAI framework is provided.

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“…As described in [16][17], by the analysis of both qualitative and quantitative data has been highly applied to solve a wide-range of practical managerial problems and decision making. Bakari [13] mentioned that few researchers would disagree with the benefit of integrating decision support system and expert system, which is the integrated system termed as Intelligent Support System or Expert Support System which comprises the knowledge of selected experts from organization because of the difficulties faced during the knowledge acquisition of knowledge based system development [21].…”

Section: Data-driven Decision Support Systemmentioning

confidence: 99%

“…According to [21], in recent time plenty of computer systems are integrating modelling, domain knowledge and analysis part of systems to assist the user intelligently. The knowledge base part of the systems used in formulating real-world problems and formulating decision model, and in analyzing and interpreting the outputs.…”

Section: Data-driven Decision Support Systemmentioning

confidence: 99%

Hybrid Decision Support System Framework for Crop Yield Prediction and Recommendation

Chiche¹

2019

IJC

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In this paper, a hybrid decision support system is presented which uses both quantitative and qualitative data to provide effective and efficient decision making for crop yield prediction and suggestion. Our framework integrates KD-DSS and DD-DSS for solving complex problems by complementing the existing gap of individual decision support system in agriculture domain. For analyzing collected quantitative data of agriculture research center, our framework uses artificial neural network as a data mining technique. So, we use ANN for uncovering hidden knowledge in stored dataset. And this knowledge is further integrated with the knowledge base developed by acquiring qualitative data from expertise and represented using an IF-THEN production rule. The integration of knowledge collected from both qualitative and quantitative source of data provides a potential advantage for solving complex problems for decision makers. Finally, we will have the opportunity to enhance the framework coupling the features which can provide a group knowledge sharing among decision makers. So, this feature can present the opportunities to fill the disparity of decisions made by different decision makers.

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Integration of neural networks with knowledge-based systems

Cited by 10 publications

References 7 publications

Explainable AI Framework for Multivariate Hydrochemical Time Series

Explainable AI Framework for Multivariate Hydrochemical Time Series

Explainable AI Framework for Multivariate Hydrochemical Time Series

Hybrid Decision Support System Framework for Crop Yield Prediction and Recommendation

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