Tinjauan Singkat Perkembangan Case–based Reasoning

Case-Based Reasoning (CBR) merupakan sebuah pendekatan penyelesaian masalah dengan menekankan peran pengalaman sebelumnya. Permasalahan baru dapat diselesaikan dengan memanfaatkan kembali dan mungkin malakukan penyesuaian terhadap permasalahan yang memiliki kesamaan yang telah diselesaikan sebelumnya. Case-Based Reasoning (CBR) telah berhasil diaplikasikan untuk penyelesaian masalah pada berbagai bidang. Pada paper ini disajikan survey atau review yang berisi tinjauan singkat perkembangan Case-Based Reasoning(CBR) berikut bidang aplikasinya

TINJAUAN SINGKAT PERKEMBANGAN CASE–BASED REASONING

Case-Based Reasoning (CBR) merupakan sebuah pendekatan penyelesaian masalah dengan menekankan peran pengalaman sebelumnya. Permasalahan baru dapat diselesaikan dengan memanfaatkan kembali dan mungkin malakukan penyesuaian terhadap permasalahan yang memiliki kesamaan yang telah diselesaikan sebelumnya. Case-Based Reasoning (CBR) telah berhasil diaplikasikan untuk penyelesaian masalah pada berbagai bidang. Pada paper ini disajikan survey atau review yang berisi tinjauan singkat perkembangan Case-Based Reasoning(CBR) berikut bidang aplikasinya

TINJAUAN SINGKAT PERKEMBANGAN CASE–BASED REASONING

Case-Based Reasoning (CBR) merupakan sebuah pendekatan penyelesaian masalah dengan menekankan peran pengalaman sebelumnya. Permasalahan baru dapat diselesaikan dengan memanfaatkan kembali dan mungkin malakukan penyesuaian terhadap permasalahan yang memiliki kesamaan yang telah diselesaikan sebelumnya. Case-Based Reasoning (CBR) telah berhasil diaplikasikan untuk penyelesaian masalah pada berbagai bidang. Pada paper ini disajikan survey atau review yang berisi tinjauan singkat perkembangan Case-Based Reasoning (CBR) berikut bidang aplikasinya

Case-based maintenance : Structuring and incrementing the Case.

International audienceTo avoid performance degradation and maintain the quality of results obtained by the case-based reasoning (CBR) systems, maintenance becomes necessary, especially for those systems designed to operate over long periods and which must handle large numbers of cases. CBR systems cannot be preserved without scanning the case base. For this reason, the latter must undergo maintenance operations.The techniques of case base’s dimension optimization is the analog of instance reduction size methodology (in the machine learning community). This study links these techniques by presenting case-based maintenance in the framework of instance based reduction, and provides: first an overview of CBM studies, second, a novel method of structuring and updating the case base and finally an application of industrial case is presented.The structuring combines a categorization algorithm with a measure of competence CM based on competence and performance criteria. Since the case base must progress over time through the addition of new cases, an auto-increment algorithm is installed in order to dynamically ensure the structuring and the quality of a case base. The proposed method was evaluated through a case base from an industrial plant. In addition, an experimental study of the competence and the performance was undertaken on reference benchmarks. This study showed that the proposed method gives better results than the best methods currently found in the literature

Retrieval, reuse, revision and retention in case-based reasoning

El original está disponible en www.journals.cambridge.orgCase-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention.Peer reviewe

Meta-data to enhance case-based prediction.

The focus of this thesis is to measure the regularity of case bases used in Case-Based Prediction (CBP) systems and the reliability of their constituent cases prior to the system's deployment to influence user confidence on the delivered solutions. The reliability information, referred to as meta-data, is then used to enhance prediction accuracy. CBP is a strain of Case-Based Reasoning (CBR) that differs from the latter only in the solution feature which is a continuous value. Several factors make implementing such systems for prediction domains a challenge. Typically, the problem and solution spaces are unbounded in prediction problems that make it difficult to determine the portions of the domain represented by the case base. In addition, such problem domains often exhibit complex and poorly understood interactions between features and contain noise. As a result, the overall regularity in the case base is distorted which poses a hindrance to delivery of good quality solutions. Hence in this research, techniques have been presented that address the issue of irregularity in case bases with an objective to increase prediction accuracy of solutions. Although, several techniques have been proposed in the CBR literature to deal with irregular case bases, they are inapplicable to CBP problems. As an alternative, this research proposes the generation of relevant case-specific meta-data. The meta-data is made use of in Mantel's randomisation test to objectively measure regularity in the case base. Several novel visualisations using the meta-data have been presented to observe the degree of regularity and help identify suspect unreliable cases whose reuse may very likely yield poor solutions. Further, performances of individual cases are recorded to judge their reliability, which is reflected upon before selecting them for reuse along with their distance from the problem case. The intention is to overlook unreliable cases in favour of relatively distant yet more reliable ones for reuse to enhance prediction accuracy. The proposed techniques have been demonstrated on software engineering data sets where the aim is to predict the duration of a software project on the basis of past completed projects recorded in the case base. Software engineering is a human-centric, volatile and dynamic discipline where many unrecorded factors influence productivity. This degrades the regularity in case bases where cases are disproportionably spread out in the problem and solution spaces resulting in erratic prediction quality. Results from administering the proposed techniques were helpful to gain insight into the three software engineering data sets used in this analysis. The Mantel's test was very effective at measuring overall regularity within a case base, while the visualisations were learnt to be variably valuable depending upon the size of the data set. Most importantly, the proposed case discrimination system, that intended to reuse only reliable similar cases, was successful at increasing prediction accuracy for all three data sets. Thus, the contributions of this research are some novel approaches making use of meta-data to firstly provide the means to assess and visualise irregularities in case bases and cases from prediction domains and secondly, provide a method to identify unreliable cases to avoid their reuse in favour to more reliable cases to enhance overall prediction accuracy

Meta-data to enhance case-based prediction

The focus of this thesis is to measure the regularity of case bases used in Case-Based Prediction (CBP) systems and the reliability of their constituent cases prior to the system's deployment to influence user confidence on the delivered solutions. The reliability information, referred to as meta-data, is then used to enhance prediction accuracy. CBP is a strain of Case-Based Reasoning (CBR) that differs from the latter only in the solution feature which is a continuous value. Several factors make implementing such systems for prediction domains a challenge. Typically, the problem and solution spaces are unbounded in prediction problems that make it difficult to determine the portions of the domain represented by the case base. In addition, such problem domains often exhibit complex and poorly understood interactions between features and contain noise. As a result, the overall regularity in the case base is distorted which poses a hindrance to delivery of good quality solutions. Hence in this research, techniques have been presented that address the issue of irregularity in case bases with an objective to increase prediction accuracy of solutions. Although, several techniques have been proposed in the CBR literature to deal with irregular case bases, they are inapplicable to CBP problems. As an alternative, this research proposes the generation of relevant case-specific meta-data. The meta-data is made use of in Mantel's randomisation test to objectively measure regularity in the case base. Several novel visualisations using the meta-data have been presented to observe the degree of regularity and help identify suspect unreliable cases whose reuse may very likely yield poor solutions. Further, performances of individual cases are recorded to judge their reliability, which is reflected upon before selecting them for reuse along with their distance from the problem case. The intention is to overlook unreliable cases in favour of relatively distant yet more reliable ones for reuse to enhance prediction accuracy. The proposed techniques have been demonstrated on software engineering data sets where the aim is to predict the duration of a software project on the basis of past completed projects recorded in the case base. Software engineering is a human-centric, volatile and dynamic discipline where many unrecorded factors influence productivity. This degrades the regularity in case bases where cases are disproportionably spread out in the problem and solution spaces resulting in erratic prediction quality. Results from administering the proposed techniques were helpful to gain insight into the three software engineering data sets used in this analysis. The Mantel's test was very effective at measuring overall regularity within a case base, while the visualisations were learnt to be variably valuable depending upon the size of the data set. Most importantly, the proposed case discrimination system, that intended to reuse only reliable similar cases, was successful at increasing prediction accuracy for all three data sets. Thus, the contributions of this research are some novel approaches making use of meta-data to firstly provide the means to assess and visualise irregularities in case bases and cases from prediction domains and secondly, provide a method to identify unreliable cases to avoid their reuse in favour to more reliable cases to enhance overall prediction accuracy.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis