Mining Sparse and Big Data by Case-based Reasoning

Perner, Petra

doi:10.1016/j.procs.2014.08.081

Cited by 25 publications

(8 citation statements)

References 20 publications

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“…It has been effectively used for adoption problem in medical CBR systems [5], e-commerce [39], data mining [25], autonomic systems [19], travel planning [6], software engineering [7,18] and an enormous range of other applications [34,48]. Furthermore, CBR can also be used to mine big and sparse data as well [37].…”

Section: Cbr Cyclementioning

confidence: 99%

Hybrid case-base maintenance approach for modeling large scale case-based reasoning systems

Khan

Hayat

Awan

2019

Hum. Cent. Comput. Inf. Sci.

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Case-based reasoning (CBR) is one of the widely used lazy machine learning methods inspired by natural learning behavior towards solving a new problem [1, 9, 22, 50]. In lazy machine learning methods, training of the model evolves with time and presentation of more data. As the new data is presented, it continues contributing in improvement of the learning curve of the system. CBR is implemented as a learning layer onto the problem domain. Each new problem instance which needs to be resolved through this learning method is presented as a case. It is matched with the cases present in the knowledge repository called case-base. Set of the nearest neighbors of a new problem is extracted using a selected similarity measure. Solutions of the retrieved nearest neighbors of the case at hand are combined together appropriately to figure out the new solution. It may undergo a fine tuning exercise if the need arises. Finally, the new case comprising of the problem and its relative solution is added into the existing Abstract Case-based reasoning (CBR) is a nature inspired paradigm of machine learning capable to continuously learn from the past experience. Each newly solved problem and its corresponding solution is retained in its central knowledge repository called case-base. Withρ the regular use of the CBR system, the case-base cardinality keeps on growing. It results into performance bottleneck as the number of comparisons of each new problem with the existing problems also increases with the case-base growth. To address this performance bottleneck, different case-base maintenance (CBM) strategies are used so that the growth of the case-base is controlled without compromising on the utility of knowledge maintained in the case-base. This research work presents a hybrid case-base maintenance approach which equally utilizes the benefits of case addition as well as case deletion strategies to maintain the case-base in online and offline modes respectively. The proposed maintenance method has been evaluated using a simulated model of autonomic forest fire application and its performance has been compared with the existing approaches on a large case-base of the simulated case study.

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Section: Cbr Cyclementioning

confidence: 99%

Hybrid case-base maintenance approach for modeling large scale case-based reasoning systems

Khan

Hayat

Awan

2019

Hum. Cent. Comput. Inf. Sci.

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“…Hence, algorithmically, there are a wide array of methods to include in our computational arsenal, and the reader is directed toward some of the references cited at the end of this article as a guide to the computational approaches that are available (Figure 4) (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38). In the following discussion, we provide some examples of how, by utilizing these soft computing methods, we can harness the multiple V's of Big Data for knowledge discovery by taking advantage of the tools of machine learning and inference to help build an unsupervised learning framework for knowledge discovery, even when the volume or data size is relatively small.…”

Section: Predictive Models And/or Classificationsmentioning

confidence: 99%

Materials Informatics: The Materials “Gene” and Big Data

Rajan

2015

Annu. Rev. Mater. Res.

253

150

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Materials informatics provides the foundations for a new paradigm of materials discovery. It shifts our emphasis from one of solely searching among large volumes of data that may be generated by experiment or computation to one of targeted materials discovery via high-throughput identification of the key factors (i.e., "genes") and via showing how these factors can be quantitatively integrated by statistical learning methods into design rules (i.e., "gene sequencing") governing targeted materials functionality. However, a critical challenge in discovering these materials genes is the difficulty in unraveling the complexity of the data associated with numerous factors including noise, uncertainty, and the complex diversity of data that one needs to consider (i.e., Big Data). In this article, we explore one aspect of materials informatics, namely how one can efficiently explore for new knowledge in regimes of structure-property space, especially when no reasonable selection pathways based on theory or clear trends in observations exist among an almost infinite set of possibilities. 153 Annu. Rev. Mater. Res. 2015.45:153-169. Downloaded from www.annualreviews.org Access provided by Yale University -Law Library on 07/04/15. For personal use only.

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“…Therefore, CBR can be seen as a method for problem-solving as well as a method to capture new experience and make it immediately available for problem-solving. It is introduced by cognitive science community and can be seen as incremental learning (Perner, 2014). A new problem is solved by seeking a similar previous case named "source case" and by reusing its solution to solve the present problem named "target case".…”

Section: Introductionmentioning

confidence: 99%

A conversational case-based reasoning approach to assisting experts in solving professional problems

Armaghan

Renaud²

2018

Knowledge Management &Amp; E-Learning: An International Journal

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Nowadays, organizations attempt to retrieve, collect, preserve and manage knowledge and experience of experts in order to reuse them later and to promote innovation. In this sense, Experience Management is one of the important organizational issues. This article is discussed the main ideas of a future Conversational Case-Based Reasoning (CCBR) intended to assist the experts of after-sales service in a French industrial company. The aim of this research is to formalize the experience of experts in after-sales service in order to better reuse them for similar problems in future. The research opts for an action research method which consists of two main parts: description of failure and proposition of decision protocol. The data were complemented by questionnaires, documentary analysis (including technical reports and other technical documents), observation and many interviews with experts. The findings include several aspects: the formalization of Problem-solving Cards, proposing the structure of case base, as well as the framework of proposed system. These formalizations permit after-sales service experts to provide effective diagnosis and problem-solving.

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Mining Sparse and Big Data by Case-based Reasoning

Cited by 25 publications

References 20 publications

Hybrid case-base maintenance approach for modeling large scale case-based reasoning systems

Hybrid case-base maintenance approach for modeling large scale case-based reasoning systems

Materials Informatics: The Materials “Gene” and Big Data

A conversational case-based reasoning approach to assisting experts in solving professional problems

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