scholarly journals Case Based Reasoning in the Detection of Retinal Abnormalities Using Decision Trees

2015 ◽  
Vol 46 ◽  
pp. 402-408 ◽  
Author(s):  
Sreeparna Banerjee ◽  
Amrita Roy Chowdhury
Keyword(s):  
Decision Trees ◽  
Case Based Reasoning ◽  
Case Based

Simplifying decision trees: A survey

1997 ◽  
Vol 12 (01) ◽  
pp. 1-40 ◽  
Author(s):  
LEONARD A. BRESLOW ◽  
DAVID W. AHA
Keyword(s):  
Decision Trees ◽  
Data Structures ◽  
Classification Accuracy ◽  
Case Based Reasoning ◽  
Reasoning Systems ◽  
Tree Generation ◽  
Good Classification ◽  
Classification Tasks ◽  
Insight Into ◽  
Case Based

Induced decision trees are an extensively-researched solution to classification tasks. For many practical tasks, the trees produced by tree-generation algorithms are not comprehensible to users due to their size and complexity. Although many tree induction algorithms have been shown to produce simpler, more comprehensible trees (or data structures derived from trees) with good classification accuracy, tree simplification has usually been of secondary concern relative to accuracy, and no attempt has been made to survey the literature from the perspective of simplification. We present a framework that organizes the approaches to tree simplification and summarize and critique the approaches within this framework. The purpose of this survey is to provide researchers and practitioners with a concise overview of tree-simplification approaches and insight into their relative capabilities. In our final discussion, we briefly describe some empirical findings and discuss the application of tree induction algorithms to case retrieval in case-based reasoning systems.

CASE-BASED REASONING FOR KNOWLEDGE ACQUISITION SUGGESTIONS

1994 ◽  
Vol 03 (01) ◽  
pp. 23-45
Author(s):  
LEE BECKER ◽  
TODD GUAY
Keyword(s):  
Decision Tree ◽  
Knowledge Acquisition ◽  
Decision Trees ◽  
Current Knowledge ◽  
General Mechanism ◽  
Prototype System ◽  
Case Based Reasoning ◽  
Acquisition Session ◽  
Specific Kind ◽  
Case Based

Case-based suggestion (CBS) is a general mechanism for system-driven interactive knowledge acquisition. CBS applies case-based reasoning to the task of knowledge acquisition. It utilizes previously acquired knowledge embodied in cases to assist the expert during the current knowledge acquisition session. In this work we describe the general CBS technique and illustrate its use during the acquisition of a specific kind of knowledge. A system utilizing CBS was implemented in the acquisition module of a prototype system called ODS, which structures acquired diagnostic knowledge in decision trees. The algorithm used for case-based suggestion by the ODS system and a description of how the decision tree knowledge was represented in the case base is presented. Several evaluation metrics are introduced, and the application of these measures to several experiences of acquiring knowledge with ODS is discussed.

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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