Case-based reasoning
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Case-Based Learning: Beyond Classification of Feature Vectors
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Retrieving Adaptable Cases: The Role of Adaptation Knowledge in Case Retrieval
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Control
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
A process model of cased-based reasoning in problem solving
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Rules and precedents as complementary warrants
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Case-based similarity assessment: estimating adaptability from experience
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Analyzing the Performance of Spam Filtering Methods When Dimensionality of Input Vector Changes
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
| Hi-index | 0.00 |
The situation assessment and similarity components of the interpretive case-based reasoning process are integral for a successful case retrieval. However, for classification problems there are domains where it can be difficult to define sets of relevant features to extract from a problem description. Likewise it is not always obvious which of these features to apply to the similarity assessment process and what, if any, weights they should be given. We suggest learning the concept of similarity by training on a set of past situations. Rather then develop a general function, we store the knowledge gained in individual similarity comparisons as similarity cases. These similarity cases define a similarity space that can be searched to identify how new problem situations can be classified. This paper describes our approach of acquiring similarity cases in the context of a straightforward classification task. A proof of concept system was built that creates similarity cases from a repository of known spam email messages and can use the similarity cases to classify unknown messages as positive or negative examples of spam.