Case-based Reasoning Classifier Research Articles

Representative Case-Based Retrieval to Support Case-Based Reasoning for Prediction

Case-based reasoning (CBR) is used to resolve a new problem by searching in past similar situations. It is widely used for prediction. However, CBR induces many shortcomings, particularly in its retrieval phase. Many methods and techniques were provided, but have influenced differently the effectiveness of the deduced results. Also, hybridizing different methods emerged to get a better information retrieval and this reasoning manner becomes a ubiquitous issue. The present study has as an objective lending support to CBR to enable enhanced retrieving valid prediction. For this purpose, the study proposes a methodology based on hybridizing data mining with CBR. Thereby, a data mining model is used and a reduced search space solution before processing a CBR's prediction retrieval is proposed. To assess the approach, a clustering was applied to a car safety data set to generate a reduced case base. Then CBR uses it for predicting the car safety. The results show a precision over 80% and an accuracy over 82%, which are well over the classical CBR and indicate the relevance of the approach.

Inspection of Crude Oil Condition using Electronic Nose (E-Nose)

Oil and gas production and distribution processes technologies are highly complex and capital-intensive. Crude oil is a high demand commodity in Malaysia and across the world. Physical and chemical properties are used to classify crude oil in oil and gas industries. The human's nose cannot distinguish the difference of smell among various crude oils grade. Conventional approaches to detect odour are expensive and difficult to operate. Due to declining production and increasing demand, using E-nose technologies to inspect the odour condition of crude oil might be a significant change in the industries. The Case-Based Reasoning (CBR) classification method also is utilised in this project to classify crude oil conditions. As a result, all crude oil samples have their odour profile pattern extracted through the normalisation of data. The performance accuracy of the CBR classifier achieved a high rate, which is 99.31% on average. Hence, the using of E-nose and utilising CBR are excellent methods in investigating odour.

HCBC: A Hierarchical Case-Based Classifier Integrated with Conceptual Clustering

The structured case representation improves case-based reasoning (CBR) by exploring structures in the case base and the relevance of case structures. Recent CBR classifiers have mostly been built upon the attribute-value case representation rather than structured case representation, in which the structural relations embodied in their representation structure are accordingly overlooked in improving the similarity measure. This results in retrieval inefficiency and limitations on the performance of CBR classifiers. This paper proposes a hierarchical case-based classifier, HCBC, which introduces a concept lattice to hierarchically organize cases. By exploiting structural case relations in the concept lattice, a novel dynamic weighting model is proposed to enhance the concept similarity measure. Based on this similarity measure, HCBC retrieves the top-K concepts that are most similar to a new case by using a bottom-up pruning-based recursive retrieval (PRR) algorithm. The concepts extracted in this way are applied to suggest a class label for the case by a weighted majority voting. Experimental results show that HCBC outperforms other classifiers in terms of classification performance and robustness on categorical data, and also works confidently well on numeric datasets. In addition, PRR effectively reduces the search space and greatly improves the retrieval efficiency of HCBC.

An Innovative Method for Project Transaction Mode Design Based on Case-Based Reasoning: A Chinese Case Study

Aiming at the design of a project transaction mode, the case-based reasoning (CBR) method is used as a methodology to build a case-based reasoning system based on project performance predictions. Thirty-four cases are initially selected for the practical application. Based on the classical CBR, the performance forecast is added, an improved continuous variable interpolation scoring method is proposed, and three types of manual revision methods are proposed: owner’s preference for the project transaction mode, extreme value, and secondary learning. The innovative method is verified with Nanjing HF Project as an example, and the results show that the case-based reasoning system can optimize the selection and design of the project transaction mode, providing a certain guarantee for project performance and facilitating the transfer of construction experience and knowledge within the construction industry.

Classification of lubricant oil adulteration level using case-based reasoning

The main purpose of this paper is to classify lubricant oil odor-profile using Case-based Reasoning classifier. Electronic nose was used for the purpose of taking data readings for each lubricant oil smell sample. The data that have been collected will be normalized, so that the data can be evaluated in a smaller scale to establish an odor-profile for each sample. Then, the odor-profiles were classified using Case-based Reasoning (CBR) classifier. The classification performance resulting 100% successfully correct classification.

Case-based reasoning classifier based on learning pseudo metric retrieval

A learning pseudo metric case-based reasoning classification method is proposed.A new classifier is redesigned on the basis of the traditional CBR classifier.The algorithm of LPM-CBR has some anti-interference ability and good robustness.The LPM-based retrieval method can improve the quality and learning ability of CBR. In case-based reasoning (CBR) classification systems, the similarity metrics play a key role and directly affect the system's performance. Based on our previous work on the learning pseudo metrics (LPM), we propose a case-based reasoning method for pattern classification, where the widely used Euclidean distance is replaced by the LPM to measure the closeness between the target case and each source case. The same type of case as the target case can be retrieved and the category of the target case can be defined by using the majority of reuse principle. Experimental results over some benchmark datasets and a fault diagnosis of the Tennessee-Eastman (TE) process demonstrate that the proposed reasoning techniques in this paper can effectively improve the classification accuracy, and the LPM-based retrieval method can substantially improve the quality and learning ability of CBR classifiers.

A New Approach for Classifying Radiology Reports

Usually, physicians need radiology reports to diagnose their patients' illness. Radiology reports are mostly stored as unstructured text from which information retrieval is difficult. As a consequence, interpreting unstructured radiology reports automatically needs sophisticated text mining algorithms. In this paper, we propose a new approach for structuring textual radiology reports. In the proposed approach radiology experts provide a set of patterns each of which consists of items with specific properties. Using the patterns as class labels, a radiology report is then classified based on the items found in its sentences. In the proposed approach, the given radiology report is first classified by a Naive Bayes classifier to support uncertainty. Then, a Boolean classifier is applied to the report which uses exact matching to achieve high precision. Finally, a Case-Based Reasoning classifier is used to learn new patterns from the report if there is any. The experimental results show that the proposed approach can dynamically classify radiology reports with high precision.

Explanation-aware computing of the prognosis for breast cancer supported by IK-DCBRC: Technical innovation.

Background:Active research is being conducted to determine the prognosis for breast cancer. However, the uncertainty is a major obstacle in this domain of medical research. In that context, explanation-aware computing has the potential for providing meaningful interactions between complex medical applications and users, which would ensure a significant reduction of uncertainty and risks. This paper presents an explanation-aware agent, supported by Intensive Knowledge-Distributed Case-Based Reasoning Classifier (IK-DCBRC), to reduce the uncertainty and risks associated with the diagnosis of breast cancer.Methods:A meaningful explanation is generated by inferring from a rule-based system according to the level of abstraction and the reasoning traces. The computer-aided detection is conducted by IK-DCBRC, which is a multi-agent system that applies the case-based reasoning paradigm and a fuzzy similarity function for the automatic prognosis by the class of breast tumors, i.e. malignant or benign, from a pattern of cytological images.Results:A meaningful interaction between the physician and the computer-aided diagnosis system, IK-DCBRC, is achieved via an intelligent agent. The physician can observe the trace of reasoning, terms, justifications, and the strategy to be used to decrease the risks and doubts associated with the automatic diagnosis. The capability of the system we have developed was proven by an example in which conflicts were clarified and transparency was ensured.Conclusion:The explanation agent ensures the transparency of the automatic diagnosis of breast cancer supported by IK-DCBRC, which decreases uncertainty and risks and detects some conflicts.

Memory and forgetting: An improved dynamic maintenance method for case-based reasoning

The problem solving performance of a case-based reasoning (CBR) system is closely related to the quantity and quality of the cases stored in the case base. With the continuous growth of the size of the case base, the so called “swamping problem” may occur when the time cost of retrieval exceeds the benefit of the accuracy. From the perspective of cognitive science, a dynamic maintenance method improved by selective memory and intentional forgetting for CBR is proposed, which can imitate the memory function of the human brain to selectively save new cases, update the forgotten values and intentionally delete the old cases. The experiments show the effectiveness of the proposed method. The selective memory and international forgetting policy can significantly reduce the time and space complexity, and retain or improve the accuracy of the CBR classifier, thus improving the performance of CBR.

A combined data mining approach using rough set theory and case-based reasoning in medical datasets

Case-based reasoning (CBR) is the process of solving new cases by retrieving the most relevant ones from an existing knowledge-base. Since, irrelevant or redundant features not only remarkably increase memory requirements but also the time complexity of the case retrieval, reducing the number of dimensions is an issue worth considering. This paper uses rough set theory (RST) in order to reduce the number of dimensions in a CBR classifier with the aim of increasing accuracy and efficiency. CBR exploits a distance based co-occurrence of categorical data to measure similarity of cases. This distance is based on the proportional distribution of different categorical values of features. The weight used for a feature is the average of co-occurrence values of the features. The combination of RST and CBR has been applied to real categorical datasets of Wisconsin Breast Cancer, Lymphography, and Primary cancer. The 5-fold cross validation method is used to evaluate the performance of the proposed approach. The results show that this combined approach lowers computational costs and improves performance metrics including accuracy and interpretability compared to other approaches developed in the literature.

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.

A learning algorithm for feature weighting in case-based reasoning

Case-based reasoning (CBR) is a methodology for problem-solving and decision making in complex business environment. This methodology is based on the reasoning of earlier experiences to solve new problems. Among well-known methods for case retrieval, K-nearest neighbour (K-NN) is more widely used. But since it is highly sensitive to input features, irrelevant features may cause an increase in the prediction error. In this research, a learning algorithm is developed by applying performance feedback to assign weight setting. The main idea behind the algorithm is to recognise cases which are incorrectly selected as nearest neighbours due to ineffective feature weighting assignment, and attempts to iteratively modify feature weights. This approach is applied to provide suggestions for better decision making and managing strategies of products in the middle of life phase. The dataset of a department store, including various products, have been used. K-fold approach is used to evaluate the prediction accuracy rate. Empirical results are compared with artificial neural network, classical CBR, CBR-GA algorithms. The results indicate that the predictive performance of the model, compare with two CBR-based algorithms, in specific case is more effective.

Case-based reasoning ensemble and business application: A computational approach from multiple case representations driven by randomness

Case-based reasoning (CBR) holds the unique capability of making predictions as well as suggestions to corporate executives and organizational decision-makers. How to improve its predictive performance is critical. This research aims to explore an ensemble of CBR from multiple case representations as an alternative to traditional approaches, which aims to produce lower errors than its member CBR predictors and independent CBR predictors and produce better performance in business failure prediction (BFP). This method is to base the member CBR predictors on randomly generated feature subsets in order to produce diversity in them. As a result, the CBR ensemble needs not to consider the two difficult/challenging tasks in BFP, i.e., the optimization of a single CBR and the search of optimal single CBR for a specific problem. We statistically validated the results of the CBR ensemble by comparing them with those of multivariate discriminant analysis, logistic regression, and the classical CBR algorithm. The results from Chinese short-term BFP indicate that the CBR ensemble significantly improves predictive ability of CBR.

Principal component case-based reasoning ensemble for business failure prediction

Case-based reasoning (CBR) has several advantages for business failure prediction (BFP), including ease of understanding, explanation, and implementation and the ability to make suggestions on how to avoid failure. We constructed a new ensemble method of CBR that we termed principal component CBR ensemble (PC-CBR-E): it, was intended to improve the predictive ability of CBR in BFP by integrating the feature selection methods in the representation level, a hybrid of principal component analysis with its two classical CBR algorithms at the modeling level and weighted majority voting at the ensemble level. We statistically validated our method by comparing it with other methods, including the best base model, multivariate discriminant analysis, logistic regression, and the two classical CBR algorithms. The results from a one-tailed significance test indicated that PC-CBR-E produced superior predictive performance in Chinese short-term and medium-term BFP.

Ensembles of case‐based reasoning classifiers in high‐dimensional biological domains

AbstractIn order to extend the capabilities of case‐based reasoning (CBR), we implemented an ensemble for case‐based reasoning (E4CBR) approach where an ensemble of CBR classifiers is combined with clustering and feature selection. We first select a subset of features of all the cases, and then cluster the cases into disjoint groups, where each group of cases forms the case‐base of one of the member classifiers. Finally, in each case‐base, a subset of features is ‘locally’ selected individually. To predict the label of an unseen case, each classifier in the ensemble provides a prediction, and the aggregation component of E4CBR combines the predictions by weighing each classifier using a CBR approach—a classifier with more cases similar to the test case receives a higher weight.We evaluated E4CBR on four publicly available biological data sets, and also compared the classification error of E4CBR with a single CBR classifier. In our experiments, we use TA3—a computational framework for CBR systems. Our results show that E4CBR reduces the classification error of our CBR classifier. On the basis of empirical results, our aggregation method outperforms the existing CBR aggregation methods. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 164‐171 DOI: 10.1002/widm.22This article is categorized under: Algorithmic Development > Ensemble Methods

Rough set based approaches to feature selection for Case-Based Reasoning classifiers

This paper investigates feature selection based on rough sets for dimensionality reduction in Case-Based Reasoning classifiers. In order to be useful, Case-Based Reasoning systems should be able to manage imprecise, uncertain and redundant data to retrieve the most relevant information in a potentially overwhelming quantity of data. Rough Set Theory has been shown to be an effective tool for data mining and for uncertainty management. This paper has two central contributions: (1) it develops three strategies for feature selection, and (2) it proposes several measures for estimating attribute relevance based on Rough Set Theory. Although we concentrate on Case-Based Reasoning classifiers, the proposals are general enough to be applicable to a wide range of learning algorithms. We applied these proposals on twenty data sets from the UCI repository and examined the impact of feature selection over classification performance. Our evaluation shows that all three proposals benefit the basic Case-Based Reasoning system. They also present robustness in comparison to well-known feature selection strategies.

Predicting business failure using forward ranking-order case-based reasoning

With the rapid development of business computing for Chinese listed companies, it is focused on to use case-based reasoning (CBR) in business failure prediction (BFP). Ranking-order case-based reasoning (RCBR) uses ranking-order information among cases to calculate similarity in the framework of k-nearest neighbor. RCBR is sensitive to the choice of features, meaning that optimal features can help it produce better performance. In this research, we attempt to use wrapper approach to find the optimal feature subset for RCBR in BFP. Forward feature selection method and RCBR are combined to construct a new method, namely forward RCBR (FRCBR). The combination is implemented by combining forward feature selection with RCBR as a wrapper module. Hold out method is used to assessing the performance of the classifier. Empirical data were collected from Chinese listed companies in the Shenzhen Stock Exchange and Shanghai Stock Exchange. We employed the standalone RCBR, the classical CBR with Euclidean metric as its heart, the inductive CBR, the two statistical methods of logistic regression and multivariate discriminate analysis (MDA), and support vector machines to make comparisons. For comparative methods, stepwise MDA was employed to select optimal feature subset. Empirical results indicated that FRCBR can produce dominating performance in short-term BFP of Chinese listed companies.

Forecasting business failure in China using case‐based reasoning with hybrid case respresentation

AbstractCase‐based reasoning (CBR) is a very effective and easily understandable method for solving real‐world problems. Business failure prediction (BFP) is a forecasting tool that helps people make more precise decisions. CBR‐based BFP is a hot topic in today's global financial crisis. Case representation is critical when forecasting business failure with CBR. This research describes a pioneer investigation on hybrid case representation by employing principal component analysis (PCA), a feature extraction method, along with stepwise multivariate discriminant analysis (MDA), a feature selection approach. In this process, sample cases are represented with all available financial ratios, i.e., features. Next, the stepwise MDA is used to select optimal features to produce a reduced‐case representation. Finally, PCA is employed to extract the final information representing the sample cases. All data signified by hybrid case representation are recorded in a case library, and the k‐nearest‐neighbor algorithm is used to make the forecasting. Thus we constructed a hybrid CBR (HCBR) by integrating hybrid case representation into the forecasting tool. We empirically tested the performance of HCBR with data collected for short‐term BFP of Chinese listed companies. Empirical results indicated that HCBR can produce more promising prediction performance than MDA, logistic regression, classical CBR, and support vector machine. Copyright © 2009 John Wiley & Sons, Ltd.

On performance of case-based reasoning in Chinese business failure prediction from sensitivity, specificity, positive and negative values

Case-based reasoning (CBR) is a machine learning technique of high performance in classification problems, and it is also a chief method in predicting business failure. Recently, several techniques have been introduced into the life-cycle of CBR for business failure prediction (BFP). The drawback of former researches on CBR-based BFP is that they only use total predictive accuracy when assessing CBR. In this research, we provide evidence on performance of CBR in Chinese BFP from various views of sensitivity, specificity, positive and negative values. Data are collected from Shanghai Stock Exchange and Shenzhen Stock Exchange in China. And we present how data are preprocessed from the view of data mining. The classical CBR model on the base of Euclidean metric, the grey CBR model on the base of grey coefficient metric, and the pseudo CBR model on the base of pseudo outranking relations are employed to make a comparative study on CBR's predictive performance in BFP. Meanwhile, support vector machine (SVM) is employed to be a baseline model for comparison. The results indicate that pseudo CBR produces better performance in Chinese BFP than classical CBR and grey CBR significantly on the whole, and it outperforms SVM marginally by total predictive accuracy and sensitivity, while it is not significantly worse than SVM by specificity.

Business failure prediction using hybrid 2 case-based reasoning (H 2CBR)

We have investigated business failure prediction (BFP) by a combination of decision-aid, statistical, and artificial intelligence techniques. The goal is to construct a hybrid forecasting method for BFP by combining various outranking preference functions with case-based reasoning (CBR), whose heart is the k-nearest neighbor ( k-NN) algorithm, and to empirically test the predictive performance of its modules. The hybrid 2 CBR (H 2CBR) forecasting method was constructed by integrating six hybrid CBR modules. These hybrid CBR modules were built up by combining and modifying six outranking preference functions with the algorithm of k-NN inside CBR. A trial-and-error iterative process was employed to identify the optimal hybrid CBR module of the H 2CBR forecasting system. The prediction of the optimal module is the final output of the H 2CBR forecasting method. We have compared the predictive performance of the six hybrid CBR modules in BFP of Chinese listed companies. In this empirical study, the classical CBR algorithm based on the Euclidean metric, and the two classical statistical methods of logistic regression (Logit) and multivariate discriminant analysis (MDA) were used as baseline models for comparison. Feature subsets were selected with the stepwise method of MDA. The predictive performance of the H 2CBR system is promising; the most preferred hybrid CBR for short-term BFP of Chinese listed companies is based on the ranking-order preference function.