Weight optimization for case-based reasoning using membrane computing

A heterogeneous multi-attribute case retrieval method based on neutrosophic sets and TODIM for emergency situations

Heterogeneous multi-attribute case retrieval is a crucial step in generating emergency alternatives during the course of emergency decision making (EDM) by referring to historical cases. This paper develops a heterogeneous multi-attribute case retrieval method for EDM that considers five attribute formats: crisp numbers, interval numbers, intuitionistic fuzzy numbers, single-valued neutrosophic numbers (SvNNs), and interval-valued neutrosophic numbers (IvNNs). First, we propose a similarity measurement of IvNNs and calculate the attribute similarities for the five attribute formats. The attribute weights are established using an optimal model. Next, the case similarities are calculated and the set of the similar historical cases is constructed. Furthermore, the evaluated information based on heterogeneous multi-attribute from similar historical cases is provided, and the calculation method for the evaluation of utility based on TODIM (an acronym for interactive and multi-criteria decision-making in Portugese) is proposed. The most suitable historical case is determined based on the case similarity and the evaluated utility. From this, the emergency alternative is generated. Finally, we demonstrate the efficacy of the proposed method with a case study and conduct comparisons against the performance of existing methods to assess the validity and superiority of the proposed method.

Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning

Fault detection in the waste incineration process depends on high-temperature image observation and the experience of field maintenance personnel, which is inefficient and can easily cause misjudgment of the fault. In this paper, a fault detection method is proposed by combining stochastic configuration networks (SCNs) and case-based reasoning (CBR). First, a learning pseudo metric method based on SCNs (SCN-LPM) is proposed by training SCN learning models using a training sample set and defined pseudo-metric criteria. Then, the SCN-LPM method is used for the case retrieval stage in CBR to construct the fault detection model based on SCN-CBR, and the structure, algorithmic implementation, and algorithmic steps are given. Finally, the performance is tested using historical data of the MSW incineration process, and the proposed method is compared with typical classification methods, such as a Back Propagation (BP) neural network, a support vector machine, and so on. The results show that this method can effectively improve the accuracy of fault detection and reduce the time complexity of the task and maintain a certain application value.

A Decision Method for Construction Safety Risk Management Based on Ontology and Improved CBR: Example of a Subway Project

Early decision-making and the prevention of construction safety risks are very important for the safety, quality, and cost of construction projects. In the field of construction safety risk management, in the face of a loose, chaotic, and huge information environments, how to design an efficient construction safety risk management decision support method has long been the focus of academic research. An effective approach to safety management is to structuralize safety risk knowledge, then identify and reuse it, and establish a scientific and systematic construction safety risk management decision system. Based on ontology and improved case-based reasoning (CBR) methods, this paper proposes a decision-making approach for construction safety risk management in which the reasoning process is improved by integrating a similarity algorithm and correlation algorithm. Compared to the traditional CBR approach in which only the similarity of information is considered, this method can avoid missing important correlated information by making inferences from multiple sources of information. Finally, the method is applied to the safety risks of subway construction for verification to show that the method is effective and easy to implement.

A Case-Based Reasoning Model for Retrieving Window Replacement Costs through Industry Foundation Class

Building information modeling (BIM) provides facility managers with a large database consisting of 3D geometric data as well as management data. In particular, Industry Foundation Class (IFC) has been applied in many studies as it provides extensive and diverse information regarding building components. With the use of BIM combined with case-based reasoning (CBR), in this study, a model was developed to estimate replacement costs by retrieving cost information from IFC. This study focused on the replacement of windows for office buildings, and the costs associated with that replacement. Two main advantages were identified in the proposed approach. First, the replacement information required for the comparison of different cases is automatically obtained from a BIM file and parsed for predicting a cost estimate using IFC. Next, the accuracy is increased by matching various cost-related data such as contractors and manufacturers in the estimation of replacement costs with the help of CBR.

A rank weighted classification for plasma proteomic profiles based on case-based reasoning

Background

It is a challenge to precisely classify plasma proteomic profiles into their clinical status based solely on their patterns even though distinct patterns of plasma proteomic profiles are regarded as potential to be a biomarker because the profiles have large within-subject variances.

Methods

The present study proposes a rank-based weighted CBR classifier (RWCBR). We hypothesized that a CBR classifier is advantageous when individual patterns are specific and do not follow the general patterns like proteomic profiles, and robust feature weights can enhance the performance of the CBR classifier. To validate RWCBR, we conducted numerical experiments, which predict the clinical status of the 70 subjects using plasma proteomic profiles by comparing the performances to previous approaches.

Results

According to the numerical experiment, SVM maintained the highest minimum values of Precision and Recall, but RWCBR showed highest average value in all information indices, and it maintained the smallest standard deviation in F-1 score and G-measure.

Conclusions

RWCBR approach showed potential as a robust classifier in predicting the clinical status of the subjects for plasma proteomic profiles.

A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis

OBJECTIVE

We present the implementation and application of a case-based reasoning (CBR) system for breast cancer related diagnoses. By retrieving similar cases in a breast cancer decision support system, oncologists can obtain powerful information or knowledge, complementing their own experiential knowledge, in their medical decision making.

METHODS

We observed two problems in applying standard CBR to this context: the abundance of different types of attributes and the difficulty in eliciting appropriate attribute weights from human experts. We therefore used a distance measure named weighted heterogeneous value distance metric, which can better deal with both continuous and discrete attributes simultaneously than the standard Euclidean distance, and a genetic algorithm for learning the attribute weights involved in this distance measure automatically. We evaluated our CBR system in two case studies, related to benign/malignant tumor prediction and secondary cancer prediction, respectively.

RESULT

Weighted heterogeneous value distance metric with genetic algorithm for weight learning outperformed several alternative attribute matching methods and several classification methods by at least 3.4%, reaching 0.938, 0.883, 0.933, and 0.984 in the first case study, and 0.927, 0.842, 0.939, and 0.989 in the second case study, in terms of accuracy, sensitivity×specificity, F measure, and area under the receiver operating characteristic curve, respectively.

CONCLUSION

The evaluation result indicates the potential of CBR in the breast cancer diagnosis domain.

Case-Based Reasoning Model with Genetic Algorithms, Group Decision-Making and Template Reduction

Case retrieval, case reuse and case retention are critical to the reasoning performance of the traditional case-based reasoning (CBR) model. In this paper, the integrated use of template reduction technology (TR), genetic algorithms (GA), nearest neighbor (NN) rules and group decision-making (GDM) establishes the CBR-GDM model. First, the TR method of the case base is introduced. Then, an attribute weights optimization using GA is discussed in the case retrieval phase. After that, a case reuse method is carried out with NN and GDM. Finally, 10 data sets from UCI are used to carry out a comparison experiment by 5-fold cross-validation. The classification accuracy rate and the classification efficiency are analyzed under the small samples, before and after the data reduction. The results show that, combined with TR, GA and GDM, the pattern classification performance by CBR can be improved.