Reliability parameters to improve combination strategies in multi-expert systems
Recognition systems based on a combination of different experts have been widely investigated in the recent: past. General criteria for improving the performance of such systems are based on estimating the reliability associated with the decision of each expert, so as to suitably weight its response in the combination phase. According to the methods proposed to-date, when the expert assigns a sample to a class, the reliability of such a decision is estimated on the basis of the recognition rate obtained by the expert on the chosen class during the training phase. As a consequence, the same reliability value is associated with every decision attributing a sample to a same class, even though it seems reasonable ro take into account: its dependence on the quality of the specific sample. We propose a method for estimating the reliability of each single recognition act of an expert on the basis of information directly derived from its output. In this way, the reliability value of a decision is more properly estimated, thus allowing a more precise weighting during the combination phase. The definition of the reliability parameters for widely used classification paradigms is discussed, together with the combining rules employing them for weighting the expert opinions. The results obtained by combining four experts in order to recognise handwritten numerals from a standard character database are presented. Comparison with classical combining rules is also reported, and the advantages of the proposed approach outlined.
multi-expert systems combining rules classification reliability neural nets ocr statistical classifiers