scholarly journals Case-Based Reasoning: The Search for Similar Solutions and Identification of Outliers

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
P. S. Szczepaniak ◽  
A. Duraj
Keyword(s):  
Linear Regression ◽  
Outlier Detection ◽  
Task Type ◽  
Case Based Reasoning ◽  
Classification Methods ◽  
Bayes Classifier ◽  
Detection Process ◽  
Research Gap ◽  
Similar Solutions ◽  
Case Based

The present paper applies the case-based reasoning (CBR) technique to the problem of outlier detection. Although CBR is a widely investigated method with a variety of successful applications in the academic domain, so far, it has not been explored from an outlier detection perspective. This study seeks to address this research gap by defining the outlier case and the underlining specificity of the outlier detection process within the CBR approach. Moreover, the case-based classification (CBC) method is discussed as a task type of CBR. This is followed by the computational illustration of the approach using selected classification methods, that is, linear regression, distance-based classifier, and the Bayes classifier.

CASE-BASED SOFTWARE QUALITY PREDICTION

2000 ◽  
Vol 10 (02) ◽  
pp. 139-152 ◽  
Author(s):  
K. GANESAN ◽  
TAGHI M. KHOSHGOFTAAR ◽  
EDWARD B. ALLEN
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Software Quality ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Case Based Reasoning ◽  
Quality Prediction ◽  
Software Quality Prediction ◽  
Case Based ◽  
Better Than

Highly reliable software is becoming an essential ingredient in many systems. However, assuring reliability often entails time-consuming costly development processes. One cost-effective strategy is to target reliability-enhancement activities to those modules that are likely to have the most problems. Software quality prediction models can predict the number of faults expected in each module early enough for reliability enhancement to be effective. This paper introduces a case-based reasoning technique for the prediction of software quality factors. Case-based reasoning is a technique that seeks to answer new problems by identifying similar "cases" from the past. A case-based reasoning system can function as a software quality prediction model. To our knowledge, this study is the first to use case-based reasoning systems for predicting quantitative measures of software quality. A case study applied case-based reasoning to software quality modeling of a family of full-scale industrial software systems. The case-based reasoning system's accuracy was much better than a corresponding multiple linear regression model in predicting the number of design faults. When predicting faults in code, its accuracy was significantly better than a corresponding multiple linear regression model for two of three test data sets and statistically equivalent for the third.

scholarly journals The Analogical Practitioner: Relating Theory to Practice in Vocational Settings Using Problem Solutions, Causality, Design Patterns, Abstraction, and Case-Based Reasoning

2017 ◽  
Vol 13 (7) ◽  
pp. 1
Author(s):  
Paul Kingsley
Keyword(s):  
Design Patterns ◽  
The Body ◽  
Case Based Reasoning ◽  
Levels Of Abstraction ◽  
Cause And Effect ◽  
Body Of Knowledge ◽  
Effect Theory ◽  
The Difference ◽  
Similar Solutions ◽  
Case Based

Universally true generalizations, from which specific conclusions can be deduced, are often unavailable to the practitioner, defined as anyone carrying out an occupation or profession. Theoretical shortcomings in the body of knowledge presented by academics can be counteracted by the practitioner using his or her knowledge of problem solutions. These can be stored as particular cases or as more generalized design patterns. They will typically contain information about cause-and-effect relationships and normative information about acceptable solutions. Use can be made of these solutions by employing reasoning by analogy and case-based reasoning. Similar problems require similar solutions. Cause-and-effect theory can be generated by practitioners using abstraction from particular cases, as an alternative to enumerative induction. The difference between this theory and that of the academic can be largely one of degree of generality. There is a continuum of cause-and-effect relationships at different levels of abstraction, which does not justify the abrupt separation of the academic and practitioner worlds, which has been encouraged by a reasonable interpretation of Bernsteins work. The study of exemplary problems in vocational education can be made more effective if it is accompanied by an examination of the actual outcomes of previously proposed solutions.

scholarly journals Where do I Go: Sistema de Recomendações Turísticas

2019 ◽  
Author(s):  
Mauricio Freitas ◽  
Anita Fernandes ◽  
Mônica Da silva
Keyword(s):  
User Profile ◽  
Case Based Reasoning ◽  
Interest Points ◽  
Semantic Constraints ◽  
Similar Solutions ◽  
The City ◽  
Case Based

Abstract. This paper describes the development of Where do I Go, a Mobile Tourist Recommendations System that uses recommendation of interest points according to user profile, temporal and semantic constraints, using Case Based Reasoning (CBR). The application aims to make recommendations to tourists during the experimentation of the city, which are in accordance with their tourism preferences and the context at the time of recommendation. CBR stores knowledge around a particular domain in case format, where each case has a problem part and another solution. CBR is premised on the fact that similar problems have similar solutions, where the basis for solving new problems is previously solved problems.

Improving Performance of Case-Based Classification Using Context-Based Relevance

1997 ◽  
Vol 06 (04) ◽  
pp. 511-536 ◽  
Author(s):  
Igor Jurisica ◽  
Janice Glasgow
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Relevant Information ◽  
Classification Systems ◽  
Machine Learning Algorithms ◽  
Case Based Reasoning ◽  
Retrieval Process ◽  
Novel Approach ◽  
Similar Solutions ◽  
Case Based

Classification involves associating instances with particular classes by maximizing intra-class similarities and minimizing inter-class similarities. Thus, the way similarity among instances is measured is crucial for the success of the system. In case-based reasoning, it is assumed that similar problems have similar solutions. The case-based approach to classification is founded on retrieving cases from the case base that are similar to a given problem, and associating the problem with the class containing the most similar cases. Similarity-based retrieval tools can advantageously be used in building flexible retrieval and classification systems. Case-based classification uses previously classified instances to label unknown instances with proper classes. Classification accuracy is affected by the retrieval process – the more relevant the instances used for classification, the greater the accuracy. The paper presents a novel approach to case-based classification. The algorithm is based on a notion of similarity assessment and was developed for supporting flexible retrieval of relevant information. Case similarity is assessed with respect to a given context that defines constraints for matching. Context relaxation and restriction is used for controlling the classification accuracy. The validity of the proposed approach is tested on real-world domains, and the system's performance, in terms of accuracy and scalability, is compared to that of other machine learning algorithms.

scholarly journals Case-based reasoning and system design: An integrated approach based on ontology and preference modeling

2014 ◽  
Vol 28 (1) ◽  
pp. 49-69 ◽  
Author(s):  
Juan Camilo Romero Bejarano ◽  
Thierry Coudert ◽  
Elise Vareilles ◽  
Laurent Geneste ◽  
Michel Aldanondo ◽  
...  
Keyword(s):  
System Design ◽  
Similarity Measures ◽  
Integrated Approach ◽  
System Engineering ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Requirements Definition ◽  
Definition Of ◽  
Similar Solutions ◽  
Case Based

AbstractThis paper addresses the fulfillment of requirements related to case-based reasoning (CBR) processes for system design. Considering that CBR processes are well suited for problem solving, the proposed method concerns the definition of an integrated CBR process in line with system engineering principles. After the definition of the requirements that the approach has to fulfill, an ontology is defined to capitalize knowledge about the design within concepts. Based on the ontology, models are provided for requirements and solutions representation. Next, a recursive CBR process, suitable for system design, is provided. Uncertainty and designer preferences as well as ontological guidelines are considered during the requirements definition, the compatible cases retrieval, and the solution definition steps. This approach is designed to give flexibility within the CBR process as well as to provide guidelines to the designer. Such questions as the following are conjointly treated: how to guide the designer to be sure that the requirements are correctly defined and suitable for the retrieval step, how to retrieve cases when there are no available similarity measures, and how to enlarge the research scope during the retrieval step to obtain a sufficient panel of solutions. Finally, an example of system engineering in the aeronautic domain illustrates the proposed method. A testbed has been developed and carried out to evaluate the performance of the retrieval algorithm and a software prototype has been developed in order to test the approach. The outcome of this work is a recursive CBR process suitable to engineering design and compatible with standards. Requirements are modeled by means of flexible constraints, where the designer preferences are used to express the flexibility. Similar solutions can be retrieved even if similarity measures between features are not available. Simultaneously, ontological guidelines are used to guide the process and to aid the designer to express her/his preferences.

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

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