Combining Multiple Similarity Metrics Using a Multicriteria Approach
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The design of a CBR system involves the use of similarity metrics. For many applications, various functions can be adopted to compare case features and to aggregate them into a global similarity measure. Given the availability of multiple similarity metrics, the designer is hence left with two options in order to come up with a working system: Either select one similarity metric or try to combine multiple metrics in a super-metric. In this paper, we study how techniques borrowed from multicriteria decision aid can be applied to CBR for combining the results of multiple similarity metrics. The problem of multi-metrics retrieval is presented as an instance of the problem of ranking alternatives based on multiple attributes. Discrete methods such as ELECTRE II have been proposed by the multicriteria decision aid community to address such situations. We conducted our experiments for ranking cases with ELECTRE II, a procedure based on pairwise comparisons. We used textual cases and multiple metrics. Our results indicate that the use of a combination of metrics with a multicriteria decision aid method can increase retrieval precision and provide an advantage over weighted sum combinations especially when similarity is measured on scales that are different in nature.
KeywordsMachine Translation Statistical Machine Translation Candidate Case Case Retrieval Multicriteria Approach
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