Classification by similarity: An overview of statistical methods of case-based reasoning

摘要

There has recently been a great deal of interest in case-based reasoning, the generation of solutions to new problems using methods which have served for similar problems in the past. Much of the commonly available computer software is however concerned with “case-retrieval”. The latter involves the matching of an observation for which the outcome is not known, to a database of examples for which the outcome is known. Various types of case retrieval, or “classification by similarity” (CBS), algorithms are discussed. Several CBS algorithms, as well as various other techniques, were applied to two small datasets. Although more comparisons are required, the CBS algorithms were found to perform significantly better than a linear discriminant analysis on a predominantly binary dataset. A single-nearest-neighbor technique, first developed in the 1950s, performed particularly well on this dataset. A more sophisticated CBS algorithm, based upon a type of neural network, performed consistently well on both datasets. As CBS techniques generally encourage the researcher to work closely with databases, they should be developed further. Progress needs to be made in the identification of “good” subsets of classifier variables, as well as in bridging the gap between statistical techniques and artificial intelligence.