Case Base Maintenance for Improving Prediction Quality

Zehraoui, Farida; Kanawati, Rushed; Salotti, Sylvie

doi:10.1007/3-540-45006-8_53

Cited by 4 publications

(8 citation statements)

References 12 publications

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“…The addition of the maintenance measurement improves system results. This is tested using the same databases in CASEP [70,72]. In order to evaluate CASEP2 system, we have compared its results in use phase to those obtained using CASEP and M-SOM-ART neural network.…”

Section: Resultsmentioning

confidence: 99%

“…A variety of recommender systems has applied this CBRbased recommendation approach mainly: BROADWAY-V1 web navigation advisor [29] and BECBKB web query refinement system [37]. The CASEP sequence classification system [70,72] as well as COBRA automatic web adaptation approach [47] use also this representation. We can also cite systems proposed in [10,18], which perform prediction task in a complex field where the data are temporal sequences.…”

Section: Case Representationmentioning

confidence: 99%

“…In this phase, the cases are added to the system when the solution is not correct. 2 The update of the maintenance measurements associated to the retrieved cases and the initialization of these measurements for the added cases are done as described previously, in the same way that in CASEP [72]. The atypical part contains the learned cases, which can be used in the system just after their addition to the case base, this makes the system incremental in real time.…”

Section: Learning Phasementioning

confidence: 99%

“…In an earlier work [70,72], we have proposed and implemented a CBR system, called CASEP, for sequence classification. Main shortcomings of CASEP are the following:…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Intelligent GPS-based predictive engine control for a motor vehicle

Lee

Begg

Walters

et al. 2010

HIS

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An intelligent Global Positioning System (GPS) based control system utilises information about the current vehicle position and upcoming terrain in order to reduce vehicle fuel consumption as well as improve road safety and comfort. The development of such in-vehicle control systems has provided static and dynamic road information. The vehicle running parameters have been mathematically defined whilst the engine control algorithms were derived from a custom-built engine test-rig. As the vehicle travelled over a particular route, road information such as gradient and position was stored with the past trajectory using a Neuro-Fuzzy technique. This road information was continuously updated and replaced by new data as the vehicle moved along, thereby adjusting the engine control parameters to reflect the actual current vehicle running data. The control system essentially used a fuzzy logic derived relief map of the test route and this was further validated and corrected based on the past trajectory from the in-vehicle GPS sensor. The simulation model demonstrated the feasibility and robustness of the control system for motor vehicle control applications

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Section: Resultsmentioning

confidence: 99%

Section: Case Representationmentioning

confidence: 99%

Section: Learning Phasementioning

confidence: 99%

“…In an earlier work [70,72], we have proposed and implemented a CBR system, called CASEP, for sequence classification. Main shortcomings of CASEP are the following:…”

Section: Introductionmentioning

confidence: 99%

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Intelligent GPS-based predictive engine control for a motor vehicle

Lee

Begg

Walters

et al. 2010

HIS

View full text Add to dashboard Cite

show abstract

“…Case-based prediction refers to the use of case-based reasoning (CBR) as the prediction technique [9,10,11,12,13,14]. Predictions in CBR include time prediction as well as class prediction (e.g., predict the kind of object based on partial observations), and strategy prediction (e.g., predicting the next movement from an opponent) among others.…”

Section: Related Workmentioning

confidence: 99%

Spatial Event Prediction by Combining Value Function Approximation and Case-Based Reasoning

Li¹,

Muñoz-Ávila

Bramsen³

et al. 2009

Case-Based Reasoning Research and Development

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Abstract. This paper presents a new approach for spatial event prediction that combines a value function approximation algorithm and case-based reasoning predictors. Each of these predictors makes unique contributions to the overall spatial event prediction. The function value approximation prediction is particularly suitable to reasoning with geographical features such as the (x,y) coordinates of an event. The case-based prediction is particularly well suited to deal with non-geographical features such as the time of the event or income level of the population. We claim that the combination of these two predictors results in a significant improvement of the accuracy in the spatial event prediction compared to pure geographically-based predictions. We support our claim by reporting on an ablation study for the prediction of improvised explosive device (IED) attacks.

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