A Lazy Learning Approach to Explaining Case-Based Reasoning Solutions

2012 ◽  
pp. 241-254 ◽  
Author(s):  
David McSherry
Keyword(s):  
Case Based Reasoning ◽  
Learning Approach ◽  
Lazy Learning ◽  
Case Based

scholarly journals An Ontology-Based and Case-Based Reasoning Supported Workplace Learning Approach

2017 ◽  
pp. 333-354 ◽  
Author(s):  
Sandro Emmenegger ◽  
Knut Hinkelmann ◽  
Emanuele Laurenzi ◽  
Andreas Martin ◽  
Barbara Thönssen ◽  
...  
Keyword(s):  
Workplace Learning ◽  
Case Based Reasoning ◽  
Learning Approach ◽  
Case Based

scholarly journals Case-Based Reasoning Framework for Malaria Diagnosis

2020 ◽  
Vol 12 (6) ◽  
pp. 31-48
Author(s):  
Eshetie Gizachew Addisu ◽  
◽  
Abiot Sinamo Boltena ◽  
Samson Yohannes Amare
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Malaria Diagnosis ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Learning Approach ◽  
Tigray Region ◽  
Programming Tool ◽  
Machine Learning Approach ◽  
Case Based

Malaria is life threatening disease in Ethiopia specifically in Tigray region. Having common symptoms with other diseases makes it complex and challenging to diagnose effectively. In this paper case based reasoning framework for malaria diagnosis has been designed to diminish the challenges faced by inexperienced practitioners during malaria diagnosis and to solve the problem on shortage of health professionals. The required knowledge for this study was collected through interview and document analysis from domain experts, malaria patient history cards and other related relevant documents. In the case acquisition process the manual format of cases makes the process too challenging. Decision tree is used to model the acquired knowledge. The case structure was then constructed using the selected most determinant attributes. Machine learning approach is applied to select the most relevant features. Feature-vector case representation technique is applied to represent the collected malaria cases. Jcolibri programming tool integrated with Eclipse and Nearest Neighbor retrieval algorithm are used to design the framework. To the end based on the results we can say that the machine learning approach can be used to select most relevant attributes in diseases having several common symptoms and designing case-based diagnosis frameworks could overcome the main problems observed in health centers of Tigray. As an artifact the framework is evaluated by statistical analysis, comparative evaluation, user evaluation and other evaluation techniques. Averagely 79 % precision, 89 % recall, 91.4% accuracy and 78.8% domain expert’s evaluation was the results scored.

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.

Feature level intentions based product recommendations with case-based reasoning

2018 ◽  
Vol 6 (1) ◽  
pp. 266-274
Author(s):  
D. Teja Santosh ◽  
◽  
K.C. Ravi Kumar ◽  
P. Chiranjeevi ◽  
◽  
...  
Keyword(s):  
Case Based Reasoning ◽  
Product Recommendations ◽  
Case Based
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