Hybrid case-based reasoning system by cost-sensitive neural network for classification

Biswas, Saroj; Chakraborty, Manomita; Singh, Heisnam Rohen; Devi, Debashree; Purkayastha, Biswajit; Das, Akhil

doi:10.1007/s00500-016-2312-x

Cited by 19 publications

(6 citation statements)

References 15 publications

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“…However, it is only in the last few years, that the Korean work has been seriously revisited. Biswas and colleagues [66][67][68] revisited the sensitivity measure and several limitations of earlier weighting schemes; they transform the MLP into an AND/OR graph from which weights are extracted for use in the CBR system. On applying this graph technique to several new domains, they find that it does better than other methods.…”

Section: Korean Developments (1999-2007): Feature-weighting Tests Of ...mentioning

confidence: 99%

How Case-Based Reasoning Explains Neural Networks: A Theoretical Analysis of XAI Using Post-Hoc Explanation-by-Example from a Survey of ANN-CBR Twin-Systems

Keane

Kenny

2019

Lecture Notes in Computer Science

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This paper proposes a theoretical analysis of one approach to the eXplainable AI (XAI) problem, using post-hoc explanation-by-example, that relies on the twinning of artificial neural networks (ANNs) with case-based reasoning (CBR) systems; so-called ANN-CBR twins. It surveys these systems to advance a new theoretical interpretation of previous work and define a road map for CBR's further role in XAI. A systematic survey of 1102 papers was conducted to identify a fragmented literature on this topic and trace its influence to more recent work involving deep neural networks (DNNs). The twin-system approach is advanced as one possible coherent, generic solution to the XAI problem. The paper concludes by road-mapping future directions for this XAI solution, considering (i) further tests of feature-weighting techniques, (ii) how explanatory cases might be deployed (e.g., in counterfactuals, a fortori cases), and (iii) the unwelcome, much-ignored issue of user evaluation.

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Section: Korean Developments (1999-2007): Feature-weighting Tests Of ...mentioning

confidence: 99%

How Case-Based Reasoning Explains Neural Networks: A Theoretical Analysis of XAI Using Post-Hoc Explanation-by-Example from a Survey of ANN-CBR Twin-Systems

Keane

Kenny

2019

Lecture Notes in Computer Science

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“…Traditional CBR has four fundamental phases which are performed on each case such as retrieval, reuse, revise and retain [18]. In retrieval phase, cases are retrieved based on the similarities to the problems being matched.…”

Section: Case-based Reasoning (Cbr)mentioning

confidence: 99%

“…Biswas et al took the advantage of using ANN to determine the feature weightage for the CBR cases. The ANN tree is pruned by taking into consideration of the four aspects in determining the feature weightagessensitivity, activity, saliency and relevant [18].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network and Information Gain in Building Case-Based Reasoning for Telemarketing Prediction

Mustapha¹,

Alsufyani²

2019

ijacsa

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Ttraditionally, case-based reasoning (CBR) has been used as advanced technique for representing expert knowledge and reasoning. However, for stochastic business data such as customers' behavior and users' preferences, the knowledge cannot be extracted directly from data to build the cases in reasoning in making prediction. Artificial Neural Network that is known to be able to build model for predicting unprecedented business data is used together with Shannon Entropy and Information Gain (IG) to identify the key features. 8 attributes have been identified as key features from the 17 attributes which are based on the telemarketing data. These attributes are used to select the key features in building CBR. The weightage for the key features in the cases is obtained from the IG values. The mechanism of creating the cases based on the input from the ANN is discussed and the integration process between ANN and CBR is given. The process of integrating the ANN and CBR shows that both techniques complement each other in building a model in predicting a customer who would subscribe one of the promoted new banking services called "term deposit".

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“…Recently, CBR systems have been developed using AI techniques. These hybrid CBR systems have been coupled with rule-based reasoning (RBR) [21,22], fuzzy logic [34], data mining [35], neural networks [36], and genetic algorithms (GAs) [17,20]. Such combinations have been reported for the different steps of the CBR.…”

Section: Introductionmentioning

confidence: 99%

Similarity measures and attribute selection for case-based reasoning in transcatheter aortic valve implantation

et al. 2020

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In a clinical decision support system, the purpose of case-based reasoning is to help clinicians make convenient decisions for diagnoses or interventional gestures. Past experience, which is represented by a case-base of previous patients, is exploited to solve similar current problems using four steps-retrieve, reuse, revise, and retain. The proposed casebased reasoning has been focused on transcatheter aortic valve implantation to respond to clinical issues pertaining vascular access and prosthesis choices. The computation of a relevant similarity measure is an essential processing step employed to obtain a set of retrieved cases from a case-base. A hierarchical similarity measure that is based on a clinical decision tree is proposed to better integrate the clinical knowledge, especially in terms of case representation, case selection and attributes weighting. A case-base of 138 patients is used to evaluate the case-based reasoning performance, and retrieve-and reuse-based criteria have been considered. The sensitivity for the vascular access and the prosthesis choice is found to 0.88 and 0.94, respectively, with the use of the hierarchical similarity measure as opposed to 0.53 and 0.79 for the standard similarity measure. Ninety percent of the suggested solutions are correctly classified for the proposed metric when four cases are retrieved. Using a dedicated similarity measure, with relevant and weighted attributes selected through a clinical decision tree, the set of retrieved cases, and consequently, the decision suggested by the case-based reasoning are substantially improved over state-ofthe-art similarity measures.

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Hybrid case-based reasoning system by cost-sensitive neural network for classification

Cited by 19 publications

References 15 publications

How Case-Based Reasoning Explains Neural Networks: A Theoretical Analysis of XAI Using Post-Hoc Explanation-by-Example from a Survey of ANN-CBR Twin-Systems

How Case-Based Reasoning Explains Neural Networks: A Theoretical Analysis of XAI Using Post-Hoc Explanation-by-Example from a Survey of ANN-CBR Twin-Systems

Application of Artificial Neural Network and Information Gain in Building Case-Based Reasoning for Telemarketing Prediction

Similarity measures and attribute selection for case-based reasoning in transcatheter aortic valve implantation

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