Case-based Reasoning Strategy Research Articles

A case-based reasoning strategy of integrating case-level and covariate-level reasoning to automatically select covariates for spatial prediction

ABSTRACT Spatial prediction is essential for obtaining the spatial distribution of geographic variables and selecting appropriate covariates for this process can be challenging, especially for non-expert users. For easing the burden of selecting the appropriate covariates, two case-based reasoning strategies, namely the most-similar-case and covariate-classification strategies, have been proposed for automated covariate selection. The former may suggest nonessential covariates due to its case-level reasoning way. And the latter with covariate-level reasoning may overlook related covariates and recommend fewer covariates than the case-level reasoning. In this study, we propose a new strategy of integrating case-level and covariate-level reasoning to effectively leverage the strengths of both previous strategies while also addressing their limitations. The proposed strategy is validated through a case study of automatically selecting covariates for digital soil mapping under reasoning with a case base containing 189 cases. The leave-one-out evaluation demonstrated that our proposed strategy outperformed the previous two strategies.

Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning

Abstract. Transferability of knowledge from well-investigated areas to a new study region is gaining importance in landslide hazard research. Considering the time-consuming compilation of landslide inventories as a prerequisite for landslide susceptibility mapping, model transferability can be key to making hazard-related information available to stakeholders in a timely manner. In this paper, we compare and combine two important transfer-learning strategies for landslide susceptibility modeling: case-based reasoning (CBR) and domain adaptation (DA). Care-based reasoning gathers knowledge from previous similar situations (source areas) and applies it to solve a new problem (target area). Domain adaptation, which is widely used in computer vision, selects data from a source area that has a similar distribution to the target area. We assess the performances of single- and multiple-source CBR, DA, and CBR–DA strategies to train and combine landslide susceptibility models using generalized additive models (GAMs) for 10 study areas with various resolutions (1, 10, and 25 m) located in Austria, Ecuador, and Italy. The performance evaluation shows that CBR and combined CBR–DA based on our proposed similarity criterion were able to achieve performances comparable to benchmark models trained in the target area itself. Particularly the CBR strategies yielded favorable results in both single- and multi-source strategies. Although DA tended to have overall lower performances than CBR, it had promising results in scenarios where the source–target similarity was low. We recommend that future transfer-learning research for landslide susceptibility modeling can build on the similarity criterion we used, as it successfully helped to transfer landslide susceptibility models by identifying suitable source regions for model training.

Strategic response for ease of doing business using case-based reasoning

Sound ease of doing business is essential for building a modernized economy and fostering high-quality growth. Research on the ease of doing business should not only focus on the impact and evaluation system, but also have guiding significance for decision-making related to the ease of doing business, but there are still relatively few studies in this area. Due to the complexity of ease of doing business, to save energy and make the most of the experience knowledge of decision-makers, we have introduced case-based reasoning (CBR) into strategy response, as a possible application of the analysis of case histories. The purpose of this paper is to propose a CBR strategy response method for ease of doing business, which can be used to formulate more intelligent and efficient ease of doing business strategy. The innovation of this research focuses on three key parts. Firstly, a CBR strategy response method for ease of doing business is innovatively developed. Secondly, the ontology method is used to build ease of doing business domain model, including city features, ease of doing business indicators, and response strategy. Thirdly, Missing values are also included in the calculation process. During the calculation process, missing values are also processed, and inappropriate response strategies in similar case sets are adjusted in response to differences between scenarios to generate feasible strategies. Finally, a case study of a city shows that this method can extend the CBR approach to ease of doing business strategy response.

Design and Application of a Case Analysis System for Handling Power Grid Operational Accidents Based on Case-Based Reasoning

In recent years, power grid accidents have occurred frequently and higher requirements have been placed on their safety operation. In current safety management in the world, there is an effective practice that uses a unified standard for structuring an accident case database and based on that database, conducts quantitative analysis to cope with accident risks. However, that is not the case for power safety management. Case-based reasoning (CBR) is such a process that solves new problems based on the solutions to similar past problems. It works by matching a current problem with historical cases and solutions in a database, in order to obtain similar case solutions or inspirations. In the matching process, if necessary, such past solutions may be modified in order to better adapt to the current actual problems. Based on the CBR method, this paper proposes how to construct a case database of power grid operational accidents, provide data support for management of power grid risks and provide knowledge services for accurate grasping of grid accident development dynamics and making quick decisions to rapidly response to the emergencies. First, it designs an operational accident case database after considering the following three aspects: case features, power grid features and accident features based on safety management theory. Secondly, in terms of how to use the power grid operational accident case database, it proposed a two-level search strategy, as well as the corresponding similarity calculation methods for different feature attributes of the case. Finally, it carried out a demonstration to verify the model by selecting four typical grid accidents. The grid database and CBR strategy proposed in this article could help China’s power grids practice intelligent analysis of grid operational accidents and improve digitalization in safety management.

Energy Optimization Using a Case-Based Reasoning Strategy.

At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.

A model for sentiment analysis based on ontology and cases

This work intends to combine domain ontology with natural language processing techniques to identify the sentiment behind judgments aiming to provide an explanation for such polarization. Also, it intends to use the Case-Based Reasoning strategy in order to learn from past reasonings (polarizations) so they can be used in new polarizations. Some steps have been developed for treatment of negation, adequacy of sentiment lexicon for a domain and adaptation of ambiguous terms classification based on past ratings. Tests were developed in two distinct areas, digital cameras and movies, to justify the model evolution until its final proposal. It was observed that the accuracy obtained by the proposed model overcomes standard statistical approaches. These results demonstrate that the model contributes to the sentiment analysis area, both as a solution that provides high levels of accuracy, as well as the possibility to present the track to achieve a particular classification.

The identification of new service opportunities: a case-based morphological analysis

Typically, firms try to differentiate their products through the integration of innovative services. For this reason, much recent research into new service development has focused on methods of identifying and generating new service ideas. The most prevalent method for generating a new service is a morphological analysis that decomposes a system into several dimensions and values, and then recombines those values to generate new services. Despite the popularity of morphological analysis, how to build morphological matrix has been an area of subjective expert judgment. In this paper, we focused on the possibility to utilize the big data to the morphological analysis to address the subjective morphological building process, by suggesting a case-based morphological analysis. By employing case-based reasoning and network analysis, firms can easily identify direct and indirect clues for the new services and integrate these results to the morphological building processes. To support this approach, this study first employs a case-based reasoning strategy to collect and identify similar services, and then assesses the patterns in those services through network analysis. By engaging in network analysis, firms can identify key aspects of new services, and determine what kinds of keywords or aspects should be employed for the dimensions and values of morphological matrices.

Incremental development of CBR strategies for computing project cost probabilities

This paper presents three CBR systems that have been developed over seven years in collaboration with two industrial partners. In this research, case based reasoning (CBR) is used to compute costs of construction projects. In contrast with previous work in the field of CBR, the focus is on choosing strategies that are compatible with user needs and characteristics. Comparing the three strategies reveals advantages and drawbacks while illustrating a “real-life” evolution of a CBR architecture in an industrial context. An important conclusion is that the ways users perform tasks have a direct influence on the best architecture for the CBR system (e.g. transformational/derivational analogy). Incremental development of strategies in the final system improves user interaction, expedites time consuming tasks and favours identification of synergy between techniques such as CBR and data mining.

Development of an Intelligent System for Tool Materials Selection in High Speed Machining

Tool materials play one of the pivotal roles in the machining system. Tool materials must be carefully chosen in relation to the workpiece material to be machined, the tool life, the metal removal rate, the machining cost, and the required accuracy and finish. The advantages and decision-making processes of case-based reasoning (CBR) are described. The CBR system for tool material selection in high speed machining (HSM) is developed. The case expression and organization, searching, matching and constraint-based adaptation rules are presented. With combining the case-based reasoning strategy and constraint-based adaptation, the tool material can be properly selected on the basis of previously successful tool materials used in HSM operations, which is helpful to push the wide applications of HSM.

CASE-BASED REASONING FOR DESIGN OF DRYING EQUIPMENT

Case-based reasoning (CBR) is based on the use of past experience in finding the solution to a new but similar problem. CBR is composed of five main phases: input of the new problem, retrieval of the most similar cases from the knowledge base, adaptation of retrieved cases, validation of the new solution, and learning of the system by adding the approved solution to the knowledge base. Adaptation is the most difficult and the most important step in the successful use of the CBR strategy. Two adaptation methods that are based on fuzzy and rough sets are presented in this paper. The given example illustrates selection of a vibrofluidized bed (VFB) dryer, determination of a drying time, and evaluation of the final moisture content based on application of both adaptation methods.

Development of a case representation strategy for a bridge design case base

This paper deals with the development of a case-based reasoning (CBR) system for bridge design. The work reported has been conducted in close collaboration with practising bridge designers. Predominantly three design engineers from a single company were used, with two engineers from different consulting engineers being used to support the work. The development of a case-based design (CBD) system is dependent on the effective representation of past designs. This dependence introduces the need for the careful consideration and development of an appropriate case representation strategy (CRS). Such a CRS has been developed for the bridge design domain and is presented here, together with the issues which have been addressed during the development of this CRS: namely, case content, knowledge acquisition and case presentation. A series of case representation concepts are presented and the merits of each discussed. The challenges of applying a CBR strategy in a complex, real-world domain such as design are also addressed.