Using Informational Confidence Values for Classifier Combination: An Experiment with Combined On-Line/Off-Line Japanese Character Recognition
Ninth International Workshop on Frontiers in Handwriting Recognition (2004)
Kokubunji, Tokyo, Japan
Oct. 26, 2004 to Oct. 29, 2004
Stefan Jaeger , University of Maryland at College Park
Classifier combination has turned out to be a powerful tool for achieving high recognition rates, especially in fields where the development of a powerful single classifier system requires considerable efforts. However, the intensive investigation of multiple classifier systems has not resulted in a convincing theoretical foundation yet. Lacking proper mathematical concepts, many systems still use empirical heuristics and ad hoc combination schemes. My paper presents an information-theoretical framework for combining confidence values generated by different classifiers. The main idea is to normalize each confidence value in such a way that it equals its informational content. Based on Shannon?s notion of information, I measure information by means of a performance function that estimates the classification performance for each confidence value on an evaluation set. Having equalized each confidence value with the information actually conveyed, I can use the elementary sum-rule to combine confidence values of different classifiers. Experiments for combined on-line/off-line Japanese character recognition show clear improvements over the best single recognition rate.
Stefan Jaeger, "Using Informational Confidence Values for Classifier Combination: An Experiment with Combined On-Line/Off-Line Japanese Character Recognition", Ninth International Workshop on Frontiers in Handwriting Recognition, vol. 00, no. , pp. 87-92, 2004, doi:10.1109/IWFHR.2004.108