Improving Performance Of Case-Based Classification Using Context-Based Relevance (1997)
| Venue: | International Journal of Artificial Intelligence Tools |
| Citations: | 2 - 1 self |
BibTeX
@ARTICLE{Jurisica97improvingperformance,
author = {Igor Jurisica and Janice Glasgow},
title = {Improving Performance Of Case-Based Classification Using Context-Based Relevance},
journal = {International Journal of Artificial Intelligence Tools},
year = {1997},
volume = {6},
pages = {3--4}
}
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Abstract
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 bas...
