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

Detecting change via competence model

In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine "when" and "how" the concept changes. In this paper, we developed a competence-based empirical distance between case chunks and then proposed a change detection method based on it. As a main contribution of our work, the change detection method provides an approach to measure the distribution change of cases of an infinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change. © 2010 Springer-Verlag

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

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

Maintenance d'un système de raisonnement à partir de cas.

International audienceLa maintenance des systèmes de Raisonnement à partir de cas intéresse un certain nombre de travaux, dont nous dressons un état de l'art. Parmi les méthodes déployées ayant trait particulièrement à la maintenance de la base de cas, nous situons notre contribution dans la réduction de la base de cas, et plus particulièrement sur une stratégie de suppression de cas basée sur un critère : la compétence. Une mesure est proposée inspirée des travaux existant dans la littérature et est illustrée par un premier test fait sur une base de 69 cas

Case Base Maintenance Approach.

International audienceCase base Maintenance is an active Case Based Reasoning research area. The main stream focuses on the method for reducing the size of the case-base while maintaining case-base competence. This paper gives an overview of these works, and proposes a case deletion strategy based on competence criteria using a novel approach. The proposed method, even if inspired from existing literature, combines an algorithm with a Competence Metric (CM). A series of tests are conducted using two standards data-sets as well as a locally constructed one, on which, three Case Base Maintenance approaches were tested. This experimental study shows how this technique compares favourably to more traditional strategies across two standard data-sets

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